Patent Publication Number: US-11645673-B2

Title: Methods and apparatus to generate corrected online audience measurement data

Description:
RELATED APPLICATIONS 
     This patent arises from a continuation of U.S. patent application Ser. No. 14/984,690, which was filed on Dec. 30, 2015, which claims benefit to U.S. Provisional Patent Application Ser. No. 62/188,482, which was filed on Jul. 2, 2015. U.S. patent application Ser. No. 14/984,690 and U.S. Provisional Patent Application Ser. No. 62/188,482 are hereby incorporated herein by reference in their entireties. Priority to U.S. patent application Ser. No. 14/984,690 and U.S. Provisional Patent Application Ser. No. 62/188,482 is claimed. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to monitoring media, and, more particularly, to methods and apparatus to generate corrected online audience measurement data. 
     BACKGROUND 
     Traditionally, audience measurement entities determine compositions of audiences exposed to media by monitoring registered panel members and extrapolating their behavior onto a larger population of interest. That is, an audience measurement entity enrolls people that consent to being monitored into a panel and collects relatively highly accurate demographic information from those panel members via, for example, in-person, telephonic, and/or online interviews. The audience measurement entity then monitors those panel members to determine media exposure information identifying media (e.g., television programs, radio programs, movies, streaming media, etc.) exposed to those panel members. By combining the media exposure information with the demographic information for the panel members, and by extrapolating the result to the larger population of interest, the audience measurement entity can determine detailed audience measurement information such as media ratings, audience composition, reach, etc. This audience measurement information can be used by advertisers to, for example, place advertisements with specific media to target audiences of specific demographic compositions. 
     More recent techniques employed by audience measurement entities monitor exposure to Internet accessible media or, more generally, online media. These techniques expand the available set of monitored individuals to a sample population that may or may not include registered panel members. In some such techniques, demographic information for these monitored individuals can be obtained from one or more database proprietors (e.g., social network sites, multi-service sites, online retailer sites, credit services, etc.) with which the individuals subscribe to receive one or more online services. However, the demographic information available from these database proprietor(s) may be self-reported and, thus, unreliable or less reliable than the demographic information typically obtained for panel members registered by an audience measurement entity. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates example client devices that report audience and/or impressions for Internet-based media to impression collection entities to facilitate identifying numbers of impressions and sizes of audiences exposed to different Internet-based media. 
         FIG.  2    is an example communication flow diagram illustrating an example manner in which an example audience measurement entity and an example database proprietor can collect impressions and demographic information associated with a client device, and can further determine ratings data from population sample data having incomplete demographic classifications in accordance with the teachings of this disclosure. 
         FIG.  3    is a block diagram of an example implementation of the audience data generator of  FIGS.  1  and/or  2   . 
         FIG.  4    is a flowchart representative of example machine readable instructions which may be executed to implement the example audience data generator of  FIGS.  1 - 3    to generate ratings information for video based on subtracting a duplicated audience count from a first audience count. 
         FIG.  5    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to perform demographic redistribution of database proprietor demographic data at an episode level. 
         FIG.  6    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to calculate a frequency and a deduplication factor associated with database proprietor demographic data at an episode level. 
         FIG.  7    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to calculate a frequency and a deduplication factor associated with database proprietor demographic data at the time segment level and the cross-publisher level. 
         FIG.  8    illustrates an example misattribution correction matrix. 
         FIG.  9    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to convert the misattribution correction matrix to an impressions count matrix. 
         FIG.  10    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to compensate impression data for inaccuracies related to demographic misattribution and database proprietor non-coverage at the episode level and the time segment level. 
         FIG.  11    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to adjust database proprietor demographic data for demographic misattribution and database proprietor non-coverage at the episode level and the time segment level. 
         FIG.  12    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to calculate ratings information at the episode level and the time segment level. 
         FIG.  13    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to adjust the ratings information at the time segment level using impression data of a time sub-segment level. 
         FIG.  14    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to adjust the ratings information at the brand level. 
         FIG.  15    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to calculate ratings information at the cross-publisher level. 
         FIG.  16    is a flowchart representative of example machine readable instructions which may be executed to implement the audience data generator of  FIGS.  1 - 3    to generate ratings information for media content types at the time segment level. 
         FIG.  17    is a block diagram of an example processor platform capable of executing the instructions of  FIGS.  4 - 7  and  9 - 16    to implement the audience data generator of  FIGS.  1 ,  2   , and/or  3 . 
     
    
    
     The figures are not to scale. Wherever appropriate, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. 
     DETAILED DESCRIPTION 
     In online audience measurement, collecting information about audiences and impressions from unknown viewers presents a risk of reporting biased demographic compositions in ratings information (e.g., average minute audience, views, duration, and unique audience). Examples disclosed herein reduce the risk of bias in the demographic compositions by correcting for sample bias and/or attribution error present in database proprietor data. 
     In disclosed examples, an online user visits a website to watch a video that has been provided with a set of instructions or information (e.g., via a software development kit (SDK) provided by an audience measurement entity such as The Nielsen Company). When the online user, who may or may not have a prior relationship with the audience measurement entity, visits the website, different types of messages are generated and sent by the online user&#39;s device via a communications network. One or more of the messages are sent to a database proprietor&#39;s server and one or more of the messages are sent to the audience measurement entity&#39;s servers. 
     The message(s) sent to the database proprietor&#39;s server include a cookie and/or other identifier that enable(s) the database proprietor to match the online user to demographic information. The database proprietor attributes the impression to a user account corresponding to the cookie value, and subsequently aggregates the impressions, sessions, and/or audience count based on the demographics associated with the user accounts. The message(s) sent to the audience measurement entity and/or the database proprietor enable the audience measurement entity and/or the database proprietor to measure the portions of the media (e.g., a video) presented at the online user&#39;s device. 
     Among the potential sources of bias in the demographic information provided by the database proprietor is coverage of the online user. For example, not everyone in a population has the database proprietor cookie that enables the database proprietor to match the impression to an online profile. For example, a user may not have an account with the database proprietor and/or the user may have an account with the database proprietor but has deleted the database proprietor cookie or otherwise does not have the database proprietor cookie set at a client device at the time of media exposure via the client device. As a result, the database proprietor is not be able to match the impressions to demographic information. A failure to match impressions results in a failure to report audience, views, or duration for those impressions and, thus, an underestimation of the audience count, view count and/or duration for the demographic group to which the online user belongs. 
     Another potential source of bias in the demographic information arises from misattribution, in which the online user&#39;s device is used by multiple users in a household. The other users may or may not have an account with database proprietor. For example, if a first user that logged into the database proprietor on the device at a first time is not the same user using the device at a second time during a media presentation, any impressions, sessions, audience, and/or duration logged based on the database proprietor cookie corresponding to the first user may be misattributed to an incorrect demographic group. As a result, misattribution may result in overestimation of the audience count and/or duration for the demographic group of the user corresponding to the cookie and underestimation of the audience count and/or duration for the demographic group of the actual user that was exposed to the media. 
     The example sources of bias described above arise in techniques for measuring online audiences for media in which viewing data is collected from unknown (e.g., anonymous) users and third-party demographic information is used to ascertain the demographic composition of the unknown users. While such techniques provide the benefit of more accurate measurements of larger audiences by including unknown or anonymous users, the use of message transmission from the client devices to the audience measurement entity and/or to the database proprietor as well as the use of cookie (or other identifier) matching at the database proprietor results in the inclusion of the above-described sources of bias in the demographic information obtained from the database proprietor. 
     Disclosed example methods to generate corrected online audience measurement data include collecting messages indicating impressions of streaming media delivered to computing devices via an Internet. Some disclosed example methods include receiving, from a database proprietor, a first audience count indicative of first numbers of the impressions corresponding to the messages attributed to the streaming media for a demographic group, a second audience count indicative of second numbers of the impressions corresponding to the messages attributed to text accessed by the demographic group, and a third audience count describing third numbers of the impressions corresponding to the messages attributed to the streaming media and the text content for accessed by the demographic group. Some disclosed example methods further include determining a corrected audience count of the streaming media for the demographic group by: estimating a fourth audience count for the demographic group and estimating a fifth audience count for the demographic group. In some examples, the fourth audience count indicative of a first number of people who accessed the streaming media and the fifth audience count indicative of a second number of people who accessed the text content. In some examples, determining a corrected audience count of the streaming media for the demographic group further includes calculating a deduplication factor for the demographic group based on the first audience count, the second audience count, and the third audience count, and applying the deduplication factor to a sum of the fourth audience count and the fifth audience count to determine the corrected audience count. Some example methods include generating audience metrics for the streaming media based on subtracting the corrected audience count from the first audience count. 
     Some example methods further include generating a first adjusted audience count by adjusting the first audience count to compensate for demographic information being unavailable to the database proprietor, generating a second adjusted audience count by adjusting the second audience count to compensate for the demographic information being unavailable to the database proprietor, and generating a third adjusted audience count by adjusting the third audience count to compensate for the demographic information being unavailable to the database proprietor. In some examples, calculating the deduplication factor is based on the first adjusted audience count, the second adjusted audience count, and the third adjusted audience count. 
     In some disclosed examples, the fourth audience count is estimated based on a first impressions count and a first frequency of impressions detected by the database proprietor. In some disclosed examples, the first impressions count indicates a presentation of a time segment of the streaming media. 
     In some disclosed example methods, the first impressions count is determined by applying a first ratio to a second impressions count, where the first ratio indicates a quantity of time sub-segments of the streaming media presented at the devices during the time segment and the second impressions count indicates a number of impressions of second media. Some example methods further include generating a redistributed impressions count by assigning second demographic information to a first impressions for which the second demographic information is unavailable to the database proprietor based on second impressions for which the second demographic information is received from the database proprietor. Some example methods further include applying an impressions matrix to the redistributed impressions count to generate the second impressions count. 
     Some example methods further include generating a misattribution matrix including a probability that the impressions are attributable to a second demographic group when the database proprietor determines the impressions to correspond to a person in a third demographic group. Some disclosed examples further include converting the misattribution matrix to the impressions matrix, where the impressions matrix indicating numbers of the impressions determined by the database proprietor to correspond to respective ones of demographic groups. 
     In some examples, the messages include an identifier relating the impressions to respective time segments of the streaming media, a network affiliate distributing the streaming media, a network affiliate distributor providing the streaming media to ones of the computing devices as permitted by the network affiliate, a program distributed by the network affiliate and provided to ones of the computing devices via the network affiliate distributor and of which the streaming media is a part, and an episode that belongs to the program of which the streaming media is at least a part. In some examples, the messages identify at least one of time segments of the streaming media that were presented at the computing devices or impressions of the text accessed via the computing devices. 
     Disclosed example apparatus to generate corrected online audience measurement data include a first impressions collector, a second impressions collector, a factor generator, an audience calculator, and a ratings data generator. In some examples, the first impressions collector collects messages indicating impressions of streaming media delivered to computing devices via an Internet. In some disclosed examples, second impressions collector receives, from a database proprietor, a first audience count indicative of first numbers of the impressions corresponding to the messages attributed to the streaming media for a demographic group, a second audience count indicative of second numbers of the impressions corresponding to the messages attributed to text accessed by the demographic group, and a third audience count describing third numbers of the impressions corresponding to the messages attributed to the streaming media and the text content for accessed by the demographic group. In some disclosed examples, the factor generator calculates a deduplication factor for the demographic group using the first audience count, the second audience count, and the third audience count. In some examples, the audience calculator determines a corrected audience count of the streaming media for the demographic group by: estimating a fourth audience count for the demographic group, estimating a fifth audience count for the demographic group, and applying the deduplication factor to a sum of the fourth audience count and the fifth audience count. In some examples, the fourth audience count indicative of a first number of people who accessed the streaming media and the fifth audience count indicative of a second number of people who accessed the text. In some examples, the ratings data generator generates ratings information for the streaming media based on subtracting the corrected audience count from the first audience count. 
     Some disclosed apparatus further include a demographic distributor to generate a first adjusted audience count by adjusting the first audience count to compensate for demographic information being unavailable to the database proprietor, generate a second adjusted audience count by adjusting the second audience count to compensate for the demographic information being unavailable to the database proprietor, and generate a third adjusted audience count by adjusting the third audience count to compensate for the demographic information being unavailable to the database proprietor. In some examples, the factor generator calculates the deduplication factor based on the first adjusted audience count, the second adjusted audience count, and the third adjusted audience count. 
     In some examples, the audience calculator estimates the fourth audience count based on a first impressions count and a first frequency of impressions detected by the database proprietor. In some examples, the first impressions count indicating presentation of a time segment of the streaming media. Some disclosed examples further includes a ratio calculator to determine the first impressions count by applying a first ratio to a second impressions count, in some examples, the first ratio indicates a quantity of time sub-segments of the streaming media presented at the devices during the time segment and the second impressions count indicates a number of impressions of second media. 
     Some example apparatus further include a matrix converter to convert a misattribution matrix to an impressions matrix, where the misattribution matrix includes a probability that the impressions are attributable to a second demographic group when the database proprietor determines the impressions to correspond to third second demographic group. In some examples, the impressions matrix indicates numbers of impressions determined by the database proprietor to correspond to respective ones of demographic groups. Some disclosed examples further include an adjuster to apply the impressions matrix to a redistributed impressions count to generate the second impressions count, in which the redistributed impressions count compensate for second demographic information being unavailable to the database proprietor. 
     In some examples, the messages include an identifier relating the impressions to respective time segments of the streaming media, a network affiliate distributing the streaming media, a network affiliate distributor providing the streaming media to ones of the computing devices as permitted by the network affiliate, a program distributed by the network affiliate and provided to ones of the computing devices via the network affiliate distributor and of which the streaming media is a part, and an episode that belongs to the program of which the streaming media is at least a part. In some example apparatus, the messages identify one of time segments of the streaming media that were presented at the computing devices or the impressions of the text at the computing devices. 
     Turning to the figures,  FIG.  1    illustrates example client devices  102  (e.g.,  102   a ,  102   b ,  102   c ,  102   d ,  102   e ) that report audience counts and/or impressions for online (e.g., Internet-based) media to impression collection entities  104  to facilitate determining numbers of impressions and sizes of audiences exposed to different online media. An “impression” generally refers to an instance of an individual&#39;s exposure to media (e.g., content, advertising, etc.). As used herein, the term “impression collection entity” refers to any entity that collects impression data, such as, for example, audience measurement entities and database proprietors that collect impression data. Duration refers to an amount of time of presentation of media, which may be credited to an impression. For example, an impression may correspond to a duration of 1 minute, 1 minute 30 seconds, 2 minutes, etc. 
     The client devices  102  of the illustrated example may be implemented by any device capable of accessing media over a network. For example, the client devices  102   a - e  may be a computer, a tablet, a mobile device, a smart television, or any other Internet-capable device or appliance. Examples disclosed herein may be used to collect impression information for any type of media. As used herein, “media” refers collectively and/or individually to content and/or advertisement(s). Media may include advertising and/or content delivered via web pages, streaming video, streaming audio, Internet protocol television (IPTV), movies, television, radio and/or any other vehicle for delivering media. In some examples, media includes user-generated media that is, for example, uploaded to media upload sites, such as YouTube, and subsequently downloaded and/or streamed by one or more other client devices for playback. Media may also include advertisements. Advertisements are typically distributed with content (e.g., programming). Traditionally, content is provided at little or no cost to the audience because it is subsidized by advertisers that pay to have their advertisements distributed with the content. 
     In the illustrated example, the client devices  102  employ web browsers and/or applications (e.g., apps) to access media. Some of the media includes instructions that cause the client devices  102  to report media monitoring information to one or more of the impression collection entities  104 . That is, when a client device  102  of the illustrated example accesses media that is instantiated with (e.g., linked to, embedded with, etc.) one or more monitoring instructions, a web browser and/or application of the client device  102  executes the one or more instructions (e.g., monitoring instructions, sometimes referred to herein as beacon instruction(s)) in the media and executes the beacon instruction(s) to cause the executing client device  102  to send a beacon request or impression request  108  to one or more impression collection entities  104  via, for example, the Internet  110 . The beacon request  108  of the illustrated example includes information about the access to the instantiated media at the corresponding client device  102  generating the beacon request. Such beacon requests allow monitoring entities, such as the impression collection entities  104 , to collect impressions for different media accessed via the client devices  102 . In this manner, the impression collection entities  104  can generate large impression quantities for different media (e.g., different content and/or advertisement campaigns). Example techniques for using beacon instructions and beacon requests to cause devices to collect impressions for different media accessed via client devices are further disclosed in U.S. Pat. No. 6,108,637 to Blumenau and U.S. Pat. No. 8,370,489 to Mainak, et al., which are incorporated herein by reference in their respective entireties. 
     The impression collection entities  104  of the illustrated example include an example audience measurement entity (AME)  114  and an example database proprietor (DP)  116 . In the illustrated example, the AME  114  does not provide the media to the client devices  102  and is a trusted (e.g., neutral) third party (e.g., The Nielsen Company, LLC, Adobe Systems, etc.) for providing accurate media access statistics. In the illustrated example, the database proprietor  116  is one of many database proprietors that operate on the Internet to provide one or more services. Such services may include, but are not limited to, email services, social networking services, news media services, cloud storage services, streaming music services, streaming video services, online shopping services, credit monitoring services, etc. Example database proprietors include social network sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, etc.), online shopping sites (e.g., Amazon.com, Buy.com, etc.), credit services (e.g., Experian), and/or any other type(s) of web service site(s) that maintain user registration records. In examples disclosed herein, the database proprietor  116  maintains user account records corresponding to users registered for Internet-based services provided by the database proprietors. That is, in exchange for the provision of services, subscribers register with the database proprietor  116 . As part of this registration, the subscriber may provide detailed demographic information to the database proprietor  116 . The demographic information may include, for example, gender, age, ethnicity, income, home location, education level, occupation, etc. In the illustrated example of  FIG.  1   , the database proprietor  116  sets a device/user identifier (e.g., an identifier described below in connection with  FIG.  2   ) on a subscriber&#39;s client device  102  that enables the database proprietor  116  to identify the subscriber in subsequent interactions. 
     In the illustrated example, when the database proprietor  116  receives a beacon/impression request  108  from a client device  102 , the database proprietor  116  requests the client device  102  to provide the device/user identifier that the database proprietor  116  had previously set for the client device  102 . The database proprietor  116  uses the device/user identifier corresponding to the client device  102  to identify demographic information in its user account records corresponding to the subscriber of the client device  102 . In this manner, the database proprietor  116  can generate “demographic impressions” by associating demographic information with an impression for the media accessed at the client device  102 . Thus, as used herein, a “demographic impression” is defined to be an impression that is associated with one or more characteristic(s) (e.g., a demographic characteristic) of the person(s) exposed to the media in the impression. Through the use of demographic impressions, which associate monitored (e.g., logged) media impressions with demographic information, it is possible to measure media exposure and, by extension, infer media consumption behaviors across different demographic classifications (e.g., groups) of a sample population of individuals. 
     In the illustrated example, the AME  114  establishes a panel of users who have agreed to provide their demographic information and to have their Internet browsing activities monitored. When an individual joins the AME panel, the person provides detailed information concerning the person&#39;s identity and demographics (e.g., gender, age, ethnicity, income, home location, occupation, etc.) to the AME  114 . The AME  114  sets a device/user identifier (e.g., an identifier described below in connection with  FIG.  2   ) on the person&#39;s client device  102  that enables the AME  114  to identify the panelist. 
     In the illustrated example, when the AME  114  receives a beacon request  108  from a client device  102 , the AME  114  requests the client device  102  to provide the AME  114  with the device/user identifier the AME  114  previously set for the client device  102 . The AME  114  uses the device/user identifier corresponding to the client device  102  to identify demographic information in its user AME panelist records corresponding to the panelist of the client device  102 . In this manner, the AME  114  can generate demographic impressions by associating demographic information with an audience for the media accessed at the client device  102  as identified in the corresponding beacon request. 
     In the illustrated example, the database proprietor  116  reports demographic impression data to the AME  114 . To preserve the anonymity of its subscribers, the demographic impression data may be anonymous demographic impression data and/or aggregated demographic impression data. In the case of anonymous demographic impression data, the database proprietor  116  reports user-level demographic impression data (e.g., which is resolvable to individual subscribers), but with any personally identifiable information (PII) removed from or obfuscated (e.g., scrambled, hashed, encrypted, etc.) in the reported demographic impression data. For example, anonymous demographic impression data, if reported by the database proprietor  116  to the AME  114 , may include respective demographic impression data for each device  102  from which a beacon request  108  was received, but with any personal identification information removed from or obfuscated in the reported demographic impression data. In the case of aggregated demographic impression data, individuals are grouped into different demographic classifications, and aggregate demographic data (e.g., which is not resolvable to individual subscribers) for the respective demographic classifications is reported to the AME  114 . In some examples, the aggregated data is aggregated demographic impression data. In other examples, the database proprietor  116  is not provided with impression data that is resolvable to a particular media name (but may instead be given a code or the like that the AME  114  can map to the code) and the reported aggregated demographic data may thus not be mapped to impressions or may be mapped to the code(s) associated with the impressions. 
     Aggregate demographic data, if reported by the database proprietor  116  to the AME  114 , may include first demographic data aggregated for devices  102  associated with demographic information belonging to a first demographic classification (e.g., a first age group, such as a group which includes ages less than 18 years old), second demographic data for devices  102  associated with demographic information belonging to a second demographic classification a second age group, such as a group which includes ages from 18 years old to 34 years old), etc. 
     As mentioned above, demographic information available for subscribers of the database proprietor  116  may be unreliable, or less reliable than the demographic information obtained for panel members registered by the AME  114 . There are numerous social, psychological and/or online safety reasons why subscribers of the database proprietor  116  may inaccurately represent or even misrepresent their demographic information, such as age, gender, etc. Accordingly, one or more of the AME  114  and/or the database proprietor  116  determine sets of classification probabilities for respective individuals in the sample population for which demographic data is collected. A given set of classification probabilities represents likelihoods that a given individual in a sample population belongs to respective ones of a set of possible demographic classifications. For example, the set of classification probabilities determined for a given individual in a sample population may include a first probability that the individual belongs to a first one of possible demographic classifications (e.g., a first age classification, such as a first age group), a second probability that the individual belongs to a second one of the possible demographic classifications (e.g., a second age classification, such as a second age group), etc. In some examples, the AME  114  and/or the database proprietor  116  determine the sets of classification probabilities for individuals of a sample population by combining, with models, decision trees, etc., the individuals&#39; demographic information with other available behavioral data that can be associated with the individuals to estimate, for each individual, the probabilities that the individual belongs to different possible demographic classifications in a set of possible demographic classifications. Example techniques for reporting demographic data from the database proprietor  116  to the AME  114 , and for determining sets of classification probabilities representing likelihoods that individuals of a sample population belong to respective possible demographic classifications in a set of possible demographic classifications, are further disclosed in U.S. Patent Publication No. 2012/0072469 (Perez et al.) and U.S. patent application Ser. No. 14/604,394 (Sullivan et al.), which are incorporated herein by reference in their respective entireties. 
     In the illustrated example, one or both of the AME  114  and the database proprietor  116  include example audience data generators to determine ratings data from population sample data having incomplete demographic classifications in accordance with the teachings of this disclosure. For example, the AME  114  may include an example audience data generator  120   a  and/or the database proprietor  116  may include an example audience data generator  120   b . As disclosed in further detail below, the audience data generator(s)  120   a  and/or  120   b  of the illustrated example process sets of classification probabilities determined by the AME  114  and/or the database proprietor  116  for monitored individuals of a sample population (e.g., corresponding to a population of individuals associated with the devices  102  from which beacon requests  108  were received) to estimate parameters characterizing population attributes (also referred to herein as population attribute parameters) associated with the set of possible demographic classifications. 
     In some examples, such as when the audience data generator  120   b  is implemented at the database proprietor  116 , the sets of classification probabilities processed by the audience data generator  120   b  to estimate the population attribute parameters include personal identification information which permits the sets of classification probabilities to be associated with specific individuals. Associating the classification probabilities enables the audience data generator  120   b  to maintain consistent classifications for individuals over time, and the audience data generator  120   b  may scrub the PII from the impression information prior to reporting impressions based on the classification probabilities. In some examples, such as when the audience data generator  120   a  is implemented at the AME  114 , the sets of classification probabilities processed by the audience data generator  120   a  to estimate the population attribute parameters are included in reported, anonymous demographic data and, thus, do not include PII. However, the sets of classification probabilities can still be associated with respective, but unknown, individuals using, for example, anonymous identifiers (e.g., hashed identifiers, scrambled identifiers, encrypted identifiers, etc.) included in the anonymous demographic data. 
     In some examples, such as when the audience data generator  120   a  is implemented at the AME  114 , the sets of classification probabilities processed by the audience data generator  120   a  to estimate the population attribute parameters are included in reported, aggregate demographic impression data and, thus, do not include personal identification and are not associated with respective individuals but, instead, are associated with respective aggregated groups of individuals. For example, the sets of classification probabilities included in the aggregate demographic impression data may include a first set of classification probabilities representing likelihoods that a first aggregated group of individuals belongs to respective possible demographic classifications in a set of possible demographic classifications, a second set of classification probabilities representing likelihoods that a second aggregated group of individuals belongs to the respective possible demographic classifications in the set of possible demographic classifications, etc. 
     Using the estimated population attribute parameters, the audience data generator(s)  120   a  and/or  120   b  of the illustrated example determine ratings data for media, as disclosed in further detail below. For example, the audience data generator(s)  120   a  and/or  120   b  may process the estimated population attribute parameters to further estimate numbers of individuals across different demographic classifications who were exposed to given media, numbers of media impressions across different demographic classifications for the given media, accuracy metrics for the estimate number of individuals and/or numbers of media impressions, numbers of individuals that accessed media multiple times from same and/or different devices, etc. 
       FIG.  2    is an example communication flow diagram  200  illustrating an example manner in which the AME  114  and the database proprietor  116  can cooperate to collect demographic impressions based on client devices  102  reporting impressions to the AME  114  and/or the database proprietor  116 .  FIG.  2    also shows the example audience data generators  120   a  and  120   b , which are able to determine ratings data from population sample data having unreliable demographic classifications in accordance with the teachings of this disclosure. The example chain of events shown in  FIG.  2    occurs when a client device  102  accesses media for which the client device  102  reports an impression to the AME  114  and/or the database proprietor  116 . In some examples, the client device  102  reports impressions for accessed media based on instructions (e.g., beacon instructions) embedded in the media that instruct the client device  102  (e.g., that instruct a web browser or an app executing on the client device  102 ) to send beacon/impression requests (e.g., the beacon/impression requests  108  of  FIG.  1   ) to the AME  114  and/or the database proprietor  116 . In such examples, the media associated with the beacon instructions is referred to as tagged media. The beacon instructions are machine executable instructions (e.g., code, a script, etc.) which may be contained in the media (e.g., in the HTML of a web page) and/or referenced by the media (e.g., identified by a link in the media that causes the client to request the instructions). 
     Although the above examples operate based on monitoring instructions associated with media (e.g., a web page, a media file, etc.), in other examples, the client device  102  reports impressions for accessed media based on instructions associated with (e.g., embedded in) apps or web browsers that execute on the client device  102  to send beacon/impression requests (e.g., the beacon/impression requests  108  of  FIG.  1   ) to the AME  114  and/or the database proprietor  116  for media accessed via those apps or web browsers. In such examples, the media itself need not be tagged media. In some examples, the beacon/impression requests (e.g., the beacon/impression requests  108  of  FIG.  1   ) include device/user identifiers (e.g., AME IDs and/or DP IDs) as described further below to allow the corresponding AME  114  and/or the corresponding database proprietor  116  to associate demographic information with resulting logged impressions. 
     In the illustrated example, the client device  102  accesses tagged media  206  that is tagged with beacon instructions  208 . The beacon instructions  208  cause the client device  102  to send a beacon/impression request  212  to an AME impressions collector  218  when the client device  102  accesses the media  206 . For example, a web browser and/or app of the client device  102  executes the beacon instructions  208  in the media  206  which instruct the browser and/or app to generate and send the beacon/impression request  212 . In the illustrated example, the client device  102  sends the beacon/impression request  212  using an HTTP (hypertext transfer protocol) request addressed to the URL (uniform resource locator) of the AME impressions collector  218  at, for example, a first Internet domain of the AME  114 . The beacon/impression request  212  of the illustrated example includes a media identifier  213  identifying the media  206  (e.g., an identifier that can be used to identify content, an advertisement, content type (e.g., text, video, a combination of both text and video), and/or any other media). In some examples, the beacon/impression request  212  also includes a site identifier (e.g., a URL) of the website that served the media  206  to the client device  102  and/or a host website ID (e.g., www.acme.com) of the website that displays or presents the media  206 . In the illustrated example, the beacon/impression request  212  includes a device/user identifier  214 . In the illustrated example, the device/user identifier  214  that the client device  102  provides to the AME impressions collector  218  in the beacon impression request  212  is an AME ID because it corresponds to an identifier that the AME  114  uses to identify a panelist corresponding to the client device  102 . In other examples, the client device  102  may not send the device/user identifier  214  until the client device  102  receives a request for the same from a server of the AME  114  in response to, for example, the AME impressions collector  218  receiving the beacon/impression request  212 . 
     In some examples, the device/user identifier  214  may be a device identifier (e.g., an international mobile equipment identity (IMEI), a mobile equipment identifier (MEID), a media access control (MAC) address, etc.), a web browser unique identifier (e.g., a cookie), a user identifier (e.g., a user name, a login ID, etc.), an Adobe Flash® client identifier, identification information stored in an HTML5 data store (where HTML is an abbreviation for hypertext markup language), and/or any other identifier that the AME  114  stores in association with demographic information about users of the client devices  102 . In this manner, when the AME  114  receives the device/user identifier  214 , the AME  114  can obtain demographic information corresponding to a user of the client device  102  based on the device/user identifier  214  that the AME  114  receives from the client device  102 . In some examples, the device/user identifier  214  may be encrypted (e.g., hashed) at the client device  102  so that only an intended final recipient of the device/user identifier  214  can decrypt the hashed identifier  214 . For example, if the device/user identifier  214  is a cookie that is set in the client device  102  by the AME  114 , the device/user identifier  214  can be hashed so that only the AME  114  can decrypt the device/user identifier  214 . If the device/user identifier  214  is an IMEI number, the client device  102  can hash the device/user identifier  214  so that only a wireless carrier (e.g., the database proprietor  116 ) can decrypt the hashed identifier  214  to recover the IMEI for use in accessing demographic information corresponding to the user of the client device  102 . By hashing the device/user identifier  214 , an intermediate party (e.g., an intermediate server or entity on the Internet) receiving the beacon request cannot directly identify a user of the client device  102 . 
     In response to receiving the beacon/impression request  212 , the AME impressions collector  218  logs an impression for the media  206  by storing the media identifier  213  contained in the beacon/impression request  212 . In the illustrated example of  FIG.  2   , the AME impressions collector  218  also uses the device/user identifier  214  in the beacon/impression request  212  to identify AME panelist demographic information corresponding to a panelist of the client device  102 . That is, the device/user identifier  214  matches a user ID of a panelist member (e.g., a panelist corresponding to a panelist profile maintained and/or stored by the AME  114 ). In this manner, the AME impressions collector  218  can associate the logged impression with demographic information of a panelist corresponding to the client device  102 . In some examples, the AME impressions collector  218  determines (e.g., in accordance with the examples disclosed in U.S. Patent Publication No. 2012/0072469 to Perez et al. and/or U.S. patent application Ser. No. 14/604,394 to Sullivan et al.) a set of classification probabilities for the panelist to include in the demographic information associated with the logged impression. As described above and in further detail below, the set of classification probabilities represent likelihoods that the panelist belongs to respective ones of a set of possible demographic classifications (e.g., such as likelihoods that the panelist belongs to respective ones of a set of possible age groupings, etc.). 
     In some examples, the beacon request  212  is an impression message, or I beacon, which is a non-durational message that is transmitted by the client device  102  when the media  206  is loaded. In some examples, the beacon/impression request  212  may not include the device/user identifier  214  (e.g., if the user of the client device  102  is not an AME panelist). In such examples, the AME impressions collector  218  logs impressions regardless of whether the client device  102  provides the device/user identifier  214  in the beacon/impression request  212  (or in response to a request for the identifier  214 ). When the client device  102  does not provide the device/user identifier  214 , the AME impressions collector  218  can still benefit from logging an impression for the media  206  even though it does not have corresponding demographics. For example, the AME  114  may still use the logged impression to generate a total impressions count and/or a frequency of impressions (e.g., an average number of impressions per unique audience member) for the media  206 . Additionally or alternatively, the AME  114  may obtain demographics information from the database proprietor  116  for the logged impression if the client device  102  corresponds to a subscriber of the database proprietor  116 . 
     In the illustrated example of  FIG.  2   , to compare or supplement panelist demographics (e.g., for accuracy or completeness) of the AME  114  with demographics from one or more database proprietors (e.g., the database proprietor  116 ), the AME impressions collector  218  returns a beacon response message  222  (e.g., a first beacon response) to the client device  102  including an HTTP “302 Found” re-direct message and a URL of a participating database proprietor  116  at, for example, a second Internet domain different than the Internet domain of the AME  114 . In the illustrated example, the HTTP “302 Found” re-direct message in the beacon response  222  instructs the client device  102  to send a second beacon request  226  to the database proprietor  116 . In other examples, instead of using an HTTP “302 Found” re-direct message, redirects may be implemented using, for example, an iframe source instruction (e.g., &lt;iframe src=“ ”&gt;) or any other instruction that can instruct a client device to send a subsequent beacon request (e.g., the second beacon request  226 ) to a participating database proprietor  116 . In the illustrated example, the AME impressions collector  218  determines the database proprietor  116  specified in the beacon response  222  using a rule and/or any other suitable type of selection criteria or process. In some examples, the AME impressions collector  218  determines a particular database proprietor to which to redirect a beacon request based on, for example, empirical data indicative of which database proprietor is most likely to have demographic data for a user corresponding to the device/user identifier  214 . In some examples, the beacon instructions  208  include a predefined URL of one or more database proprietors to which the client device  102  should send follow up beacon requests  226 . In other examples, the same database proprietor is always identified in the first redirect message (e.g., the beacon response  222 ). 
     In the illustrated example of  FIG.  2   , the beacon/impression request  226  may include a device/user identifier  227  that is a DP ID because it is used by the database proprietor  116  to identify a subscriber of the client device  102  when logging an impression. In some instances (e.g., in which the database proprietor  116  has not yet set a DP ID in the client device  102 ), the beacon/impression request  226  does not include the device/user identifier  227 . In some examples, the DP ID is not sent until the database proprietor  116  requests the same (e.g., in response to the beacon/impression request  226 ). In some examples, the device/user identifier  227  is a device identifier (e.g., an IMEI), an MEID, a MAC address, etc.), a web browser unique identifier (e.g., a cookie), a user identifier (e.g., a user name, a login ID, etc.), an Adobe Flash® client identifier, identification information stored in an HTML5 data store, and/or any other identifier that the database proprietor  116  stores in association with demographic information about subscribers corresponding to the client devices  102 . In some examples, the device/user identifier  227  may be encrypted (e.g., hashed) at the client device  102  so that only an intended final recipient of the device/user identifier  227  can decrypt the hashed identifier  227 . For example, if the device/user identifier  227  is a cookie that is set in the client device  102  by the database proprietor  116 , the device/user identifier  227  can be hashed so that only the database proprietor  116  can decrypt the device/user identifier  227 . If the device/user identifier  227  is an IMEI number, the client device  102  can hash the device/user identifier  227  so that only a wireless carrier (e.g., the database proprietor  116 ) can decrypt the hashed identifier  227  to recover the IMEI for use in accessing demographic information corresponding to the user of the client device  102 . By hashing the device/user identifier  227 , an intermediate party (e.g., an intermediate server or entity on the Internet) receiving the beacon request cannot directly identify a user of the client device  102 . For example, if the intended final recipient of the device/user identifier  227  is the database proprietor  116 , the AME  114  cannot recover identifier information when the device/user identifier  227  is hashed by the client device  102  for decrypting only by the intended database proprietor  116 . 
     In the example of  FIG.  2   , the beacon instructions  208  cause the client device  102  to transmit multiple second beacon requests  226   a ,  226   b  to the database proprietor  116  and to the AME  114 . The example second beacon requests  226   a ,  226   b  may take the form of duration (e.g., indicative of video content) messages (e.g., referred to herein as D beacons or D pings). 
     In the example of  FIG.  2   , D beacons are beacon requests  226   a ,  226   b  that contain duration information that identify portions of the media  206  (e.g., video content) that has been presented at the client device  102 . For example, the D beacons may use the format “D_s_abcde,” where s denotes a time segment of the media  206  (e.g., an episode of a program distributed by a distributor (e.g., a network affiliate distributor)) being presented at the client device  102 . The s term can vary between 1 and n, with n being the last segment of the media  206 . In the D beacon format D_s_abcde, the terms a, b, c, d, and e refer to respective ones of 1st, 2nd, 3rd, 4th, and 5th sub-segments (e.g., minutes) of a segment. A value 0 in any of the terms a, b, c, d, e indicates that the corresponding sub-segment (e.g., the corresponding minute) was not presented and the value 1 indicates that the corresponding sub-segment (e.g., the corresponding minute) was presented. Thus, a D beacon of D_2_01010 indicates that, of a second time segment of the media  206  (of n time segments making up the media  206 ), the 2nd and 4th sub-segments were presented while the 1st, 3rd, and 5th sub-segments were not presented. In the example of  FIG.  2   , the client device  102  sends the D beacons at the conclusion of designated time segments (e.g., after the 5th minute of a time segment is viewed) and/or at designated time intervals during presentation of the media (e.g., every z minutes. Where z sub-segments are represented per D beacon). As used herein, a “time segment” may refer to the larger segments represented by one D beacon (e.g., super-segments) and/or to a sub-segment (e.g., a minute) that is a component of the larger segment. 
     When the database proprietor  116  receives the device/user identifier  227 , the database proprietor  116  can obtain demographic information corresponding to a user of the client device  102  based on the device/user identifier  227  that the database proprietor  116  receives from the client device  102 . In some examples, the database proprietor  116  determines (e.g., in accordance with the examples disclosed in U.S. Patent Publication No. 2012/0072469 to Perez et al. and/or U.S. patent application Ser. No. 14/604,394 to Sullivan et al.) a set of classification probabilities associated with the user of the client device  102  to include in the demographic information associated with this user. As described above and in further detail below, the set of classification probabilities represent likelihoods that the user belongs to respective ones of a set of possible demographic classifications (e.g., likelihoods that the panelist belongs to respective ones of a set of possible age groupings, etc.). 
     Although only a single database proprietor  116  is shown in  FIGS.  1  and  2   , the impression reporting/collection process of  FIGS.  1  and  2    may be implemented using multiple database proprietors. In some such examples, the beacon instructions  208  cause the client device  102  to send beacon/impression requests  226  to numerous database proprietors. For example, the beacon instructions  208  may cause the client device  102  to send the beacon/impression requests  226  to the numerous database proprietors in parallel or in daisy chain fashion. In some such examples, the beacon instructions  208  cause the client device  102  to stop sending beacon/impression requests  226  to database proprietors once a database proprietor has recognized the client device  102 . In other examples, the beacon instructions  208  cause the client device  102  to send beacon/impression requests  226  to database proprietors so that multiple database proprietors can recognize the client device  102  and log a corresponding impression. Thus, in some examples, multiple database proprietors are provided the opportunity to log impressions and provide corresponding demographics information if the user of the client device  102  is a subscriber of services of those database proprietors. 
     In some examples, prior to sending the beacon response  222  to the client device  102 , the AME impressions collector  218  replaces site IDs (e.g., URLs) of media provider(s) that served the media  206  with modified site IDs (e.g., substitute site IDs) which are discernable only by the AME  114  to identify the media provider(s). In some examples, the AME impressions collector  218  may also replace a host website ID (e.g., www.acme.com) with a modified host site ID (e.g., a substitute host site ID) which is discernable only by the AME  114  as corresponding to the host website via which the media  206  is presented. In some examples, the AME impressions collector  218  also replaces the media identifier  213  with a modified media identifier  213  corresponding to the media  206 . In this way, the media provider of the media  206 , the host website that presents the media  206 , and/or the media identifier  213  are obscured from the database proprietor  116 , but the database proprietor  116  can still log impressions based on the modified values (e.g., if such modified values are included in the beacon request  226 ), which can later be deciphered by the AME  114  after the AME  114  receives logged impressions from the database proprietor  116 . In some examples, the AME impressions collector  218  does not send site IDs, host site IDS, the media identifier  213  or modified versions thereof in the beacon response  222 . In such examples, the client device  102  provides the original, non-modified versions of the media identifier  213 , site IDs, host IDs, etc. to the database proprietor  116 . 
     In the illustrated example, the AME impression collector  218  maintains a modified ID mapping table  228  that maps original site IDs with modified (or substitute) site IDs, original host site IDs with modified host site IDs, and/or maps modified media identifiers to the media identifiers such as the media identifier  213  to obfuscate or hide such information from database proprietors such as the database proprietor  116 . Also in the illustrated example, the AME impressions collector  218  encrypts all of the information received in the beacon/impression request  212  and the modified information to prevent any intercepting parties from decoding the information. The AME impressions collector  218  of the illustrated example sends the encrypted information in the beacon response  222  to the client device  102  so that the client device  102  can send the encrypted information to the database proprietor  116  in the beacon/impression request  226 . In the illustrated example, the AME impressions collector  218  uses an encryption that can be decrypted by the database proprietor  116  site specified in the HTTP “302 Found” re-direct message. 
     Periodically or aperiodically, the impression data collected by the database proprietor  116  is provided to a DP impressions collector  232  of the AME  114  as, for example, batch data. In some examples, the impression data collected from the database proprietor  116  by the DP impressions collector  232  is demographic impression data, which includes sets of classification probabilities for individuals of a sample population associated with client devices  102  from which beacon requests  226  were received. In some examples, the sets of classification probabilities included in the demographic impression data collected by the DP impressions collector  232  correspond to respective ones of the individuals in the sample population, and may include personal identification capable of identifying the individuals, or may include obfuscated identification information to preserve the anonymity of individuals who are subscribers of the database proprietor but not panelists of the AME  114 . In some examples, the sets of classification probabilities included in the demographic impression data collected by the DP impressions collector  232  correspond to aggregated groups of individuals, which also preserves the anonymity of individuals who are subscribers of the database proprietor. 
     Additional examples that may be used to implement the beacon instruction processes of  FIG.  2    are disclosed in U.S. Pat. No. 8,370,489 to Mainak et al. In addition, other examples that may be used to implement such beacon instructions are disclosed in U.S. Pat. No. 6,108,637 to Blumenau. 
     In the example of  FIG.  2   , the AME  114  includes the example audience data generator  120   a  of  FIG.  1    to determine ratings data (e.g., ratings information) using the sets of classification probabilities determined by the AME impressions collector  218  and/or obtained by the DP impressions collector  232 . Additionally or alternatively, in the example of  FIG.  2   , the database proprietor  116  includes the example audience data generator  120   b  of  FIG.  1    to determine media ratings data using the sets of classification probabilities determined by the database proprietor  116 . A block diagram of an example audience data generator  120 , which may be used to implement one or both of the example audience data generators  120   a  and/or  120   b , is illustrated in  FIG.  3   . 
     The example audience data generator  120  of  FIG.  3    receives impression information from the AME impressions collector  218 . Example impression information includes the impression requests  212  and/or the beacon requests  226   b . The AME impressions collector  218  collects and tracks the requests  212 ,  226   b , as well as the devices  102  from which the requests  212 ,  226   b  are received. 
     The example audience data generator  120  further receives demographic information corresponding to impressions received by the DP  116 . In some examples, the impressions received at the DP  116  correspond at least in part to the requests  212 ,  226   b  received at the AME impressions collector  218 . The example DP  116  attempts to determine demographic information for the impressions (e.g., the beacon requests  226   a ), and provides numbers of impressions, numbers of duration units (e.g., minutes, seconds, etc.), and/or numbers of audience members to the AME  114 . 
     In examples disclosed herein, the audience data generator  120  receives aggregated data files containing aggregated numbers of impressions, aggregated numbers of duration units, and aggregated counts of unique audience members organized by groups (e.g., levels of detail) from the DP  116 . In examples disclosed herein, the aggregated data files are organized by platform (e.g., computing platform). For example, the DP  116  attributes the aggregated data files to a mobile computing platform or a non-mobile computing platform. As such, the aggregated data files may indicate whether an audience accessed the media  206  of  FIG.  2    via a mobile computing platform (e.g., through a mobile device such as a smartphone, a tablet, a portable media player (PMP), etc.) or via a non-mobile computing platform (e.g., through a desktop computer). In some examples, the DP  116  may identify ones of the aggregated data files attributed to both the mobile computing platform and the non-mobile computing platform as duplicate data files and attribute the duplicate data files to a total digital computing platform. As such, the aggregated data files are organized by mobile computing platform, non-mobile computing platform, and/or total digital computing platform. 
     In examples disclosed herein, the aggregated data files are further organized by brand. As used herein, a brand refers to a network affiliate (e.g., a broadcaster, streaming entity, or media provider owned by a company other than an owner of a network by which media is delivered). For example, the DP  116  may attribute first ones of the aggregated data files to a first brand, or a first network affiliate (e.g., the American Broadcast Company (ABC)), second ones of the aggregated data files to a second brand, or a second network affiliate (e.g., the FOX Broadcasting Company), etc. In examples disclosed herein, the aggregated data files are further organized by sub-brand. As used herein, sub-brand refers to network affiliate distributors (e.g., an asset, a subsidiary, or a division of a network affiliate distributing specified services and media). For example, the DP  116  may attribute certain ones of the aggregated data files to a first sub-brand, or a first network affiliate distributor (e.g., CBS Television), a second sub-brand, or a second network affiliate distributor (e.g., CBS Sports), a third sub-brand, or a third network affiliate distributor (e.g., Hulu), etc. In examples disclosed herein, a network affiliate may grant permissions to certain ones of network affiliate distributors to allow the certain ones of the network affiliate distributors to provide certain programs and/or episodes to an audience. 
     In the example of  FIG.  3   , the audience data generator  120  receives the aggregated data files from the DP  116  organized by: Platform×Device×Brand×Corrected Age×Gender (e.g., platform and device specific brand-level (e.g., network affiliate level) aggregation by age and gender, such as “Platform 1, Device 1, Brand 1, Male, ages 18-24” and “Platform 1, Device 1, Brand 2, Male, ages 25-30”); Platform×Device×Brand×Sub-Brand×Corrected Age×Gender (e.g., platform and device specific sub-brand-level (e.g., network affiliate distributor level) aggregation by age and gender, such as “Platform 1, Device 1, Brand 1, Sub-Brand 1, Male, ages 18-24” and “Platform 1, Device 1, Brand 1, Sub-Brand 2, Male, ages 25-30”); Platform×Device×Brand×Sub-Brand×Asset×Corrected Age×Gender (e.g., platform and device specific asset-level (e.g., program level) aggregation by age and gender, such as “Platform 1, Device 1, Brand 1, Sub-brand 1, Asset 1, Male, ages 18-24” and “Platform 1, Device 1, Brand 1, Sub-brand 1, Asset 2, Male, ages 25-30”); Platform×Brand×Sub-Brand×Asset×Episode×Corrected Age×Gender (e.g., episode-level aggregation by platform, age, and gender, such as “Platform 1, Brand 1, Sub-Brand 1, Asset 1, Episode 1, Male, ages 18-24” and “Platform 1, Brand 1, Sub-brand 1, Asset 1, Episode 2, Male, ages 18-24”); Platform×Brand×Sub-Brand×Asset×Segment×Corrected Age×Gender (e.g., time segment-level aggregation by age and gender, such as “Platform 1, Brand 1, Sub-Brand 1, Asset 1, Minute 1 (or Minutes 1-5), Male, ages 18-24” and “Platform 1, Brand 1, Sub-Brand 1, Asset 1, Minute 5 (or Minutes 6-10). Male, ages 18-24”), Platform×Brand×Segment×Corrected Age×Gender (e.g., time segment-level aggregation by age and gender, such as “Platform 1, Brand 1, Minute 1 (Or Minutes 1-5), male, ages 18-24” and “Platform 1, Brand 1, Minute 5 (or Minutes 6-10), Male, ages 18-24”). 
     The audience data generator  120  also receives aggregated data files containing aggregated numbers of impressions, aggregated numbers of duration units, and aggregated counts of unique audience members organized by groups (e.g., levels of detail) observed at a monitored website (e.g., web site census counts): Platform×Device×Brand (e.g., platform and device specific brand-level (e.g., network affiliate level) aggregation, such as “Platform 1, Device 1, Brand 1” and “Platform 1, Device 1, Brand 2”); Platform×Device×Brand×Sub-Brand (e.g., platform and device specific sub-brand-level (e.g., network affiliate distributor level) aggregation, such as “Platform 1, Device 1, Brand 1, Sub-Brand 1” and “Platform 1, Device 1, Brand 1, Sub-Brand 2”); Platform×Device×Brand×Sub-Brand×Asset (e.g., platform and device specific asset-level (e.g., program level)) aggregation, such as “Platform 1, Device 1, Brand 1, Sub-Brand 1, Asset 1” and “Platform 1, Device 1, Brand 1, Sub-Brand 1, Asset 2”); Platform×Brand×Sub-Brand×Asset×Episode (e.g., episode-level aggregation, such as “Platform 1, Brand 1, Sub-Brand 1, Asset 1, Episode 1” and “Platform 1, Brand 1, Sub-brand 1, Asset 1, Episode 2)”; Platform×Brand×Sub-Brand×Asset×Segment (e.g., time segment-level aggregation by age and gender, such as “Platform 1, Brand 1, Sub-Brand 1, Asset 1, Minute 1 (or Minutes 1-5)” and “Platform 1, brand 1, Sub-Brand 1, Asset 1, Minute 5 (Or Minutes 6-10))” 
     The example audience data generator  120  includes an example calibration data collector  300 . The example calibration data collector  300  collects or obtains survey calibration data describing platform and device usage statistics of an audience. For example, the survey calibration data may include and/or be based on responses to a survey (e.g., a probability survey) of households selected at random. In some examples, a calibration survey obtains information including the demographic characteristics of a panel member (e.g., age and gender, race, ethnicity, language characteristics, household income, geographic location, etc.), the numbers and/or types of devices (e.g., smartphones, tablet computers, portable media players, desktop computer, etc.) used by a panel member, and/or registrations of panel members with specified database proprietors (e.g., DP  116  of  FIG.  1   ). In some examples, the calibration survey obtains, for each panel member of a survey panel, usage characteristics of each of the devices and/or types of devices; programs and episodes typically viewed by the panel member; programs and episodes typically viewed by the panel member on each device and/or type of device; apps used by the panel member on each device and/or type of device; and/or characteristics of interactions with specified database proprietors on each device and/or type of device. The example calibration data collector  300  obtains the survey calibration data from at least a threshold number of households and, if appropriate, weights the results to be reflective of a general population or audience. 
     In some other examples, the survey calibration data source includes a survey of established panel(s) of respondents, such as the Nielsen National People Meter (NPM) panel for television ratings. Surveys of established panels often provide higher quality survey calibration data. 
     The example audience data generator  120  includes an example platform data manager  302 , an example device data manager  304 , an example segment data manager  306 , an example episode data manager  308 , an asset data manager  310 , a sub-brand data manager  312 , and a brand data manager  314 . The example platform data manager  302 , the example device data manager  304 , the example segment data manager  306 , the example episode data manager  308 , the example asset data manager  310 , the example sub-brand data manager  312 , and the example brand data manager  314  each manage the aggregated demographic information received from the DP  116  and/or from the AME impressions collector  218  for the respective data levels. 
     The example audience data generator  120  of  FIG.  3    further includes an example DP data manager  316 . The example DP data manager  316  collects, stores, and provides the demographic information (e.g., raw impressions, duration units, and/or audience data per demographic group and/or data level) obtained from the DP  116 . While the platform data manager  302 , the device data manager  304 , the segment data manager  306 , the episode data manager  308 , the asset data manager  310 , the sub-brand data manager  312 , and the brand data manager  314  manage the correction and/or calibration of demographic information obtained from the DP  116  at the respective levels, the example DP data manager  316  manages the raw demographic information obtained from the DP  116  for use in the correction and/or calibration of the demographic information. 
     The example audience data generator  120  also includes an example demographic distributor  318 , an example distributed demographic data calculator  320 , an example matrix generator  322 , an example episode-segment (E-S) adjuster  324 , an example census imputer  326 , an example metrics generator  328 , an example segment modifier  330 , an example brand/cross-platform adjuster  332 , an example content calculator  336 , and an example ratings data generator  338 . The example platform data manager  302 , the example device data manager  304 , the example segment data manager  306 , the example episode data manager  308 , the example asset data manager  310 , the example sub-brand data manager  312 , and/or the example brand data manager  314  may communicate with or notify the example demographic distributor  318 , the example distributed demographic data calculator  320 , the example matrix generator  322 , the example episode-segment (E-S) adjuster  324 , the example census imputer  326 , the example metrics generator  328 , the example segment modifier  330 , the example brand/cross-platform adjuster  332 , the example content calculator  336 , and the example ratings data generator  338  to correct and/or calibrate the DP demographic information and/or to generate ratings information for online media. 
     The example demographic distributor  318  of  FIG.  3    may receive communications, notifications, requests, signals, etc. from the platform data manager  302 , the device data manager  304 , the segment data manager  306 , the episode data manager  308 , the asset data manager  310 , the sub-brand data manager  312 , and/or the brand data manager  314  to distribute impressions, duration units and/or audience members into demographic groups (e.g., demographic groups defined by the AME  114  and/or another entity, demographic groups corresponding to an audience measurement system such as television ratings, etc.). For example, all or a portion of impression information received from the DP  116  may have incomplete demographic information (e.g., undetermined age and/or gender information). As described in more detail below, the example demographic distributor  318  determines distributions of impressions, duration units, and/or audience members by demographic groups, and then distributes any impressions, duration units, and/or audience members for which a demographic group is unknown into the demographic groups based on the determined distribution. In examples disclosed herein, the demographic distributor  318  may send distributed (e.g., redistributed) impressions, duration units, and/or audience counts to the distributed demographic data calculator  320  and/or the content calculator  336 . Example implementations of the demographic distributor  318  are described below in connection with  FIG.  5   . 
     The example distributed demographic data calculator  320  of  FIG.  3    receives redistributed impressions, duration units, and/or audience counts from the demographic distributor  318  and calculates raw measures for the demographic groups. The distributed demographic data calculator  320  includes an example duration-per-impression (DPI) calculator  340 , an example segment frequency calculator (FC)  341 , an example segment deduplication factor generator (DFG)  342 , an example episode frequency calculator (FC)  343 , an example episode deduplication factor generator (DFG)  344 , an example asset frequency calculator (FC)  345 , an example asset deduplication factor generator (DFG)  346 , an example sub-brand frequency calculator (FC)  347 , an example sub-brand deduplication factor generator (DFG)  348 , an example brand frequency calculator (FC)  349 , an example brand deduplication factor generator (DFG)  350 , an example cross-publisher frequency calculator (CP-FC)  351 , and an example cross-publisher deduplication factor generator (CP-DFG)  352 . Example implementations of the distributed demographic data calculator  320  are described below in connection with  FIGS.  6  and  7   . 
     The DPI calculator  340  calculates a duration-per-impression (DPI) measure for each demographic group using redistributed impressions and redistributed audience counts at the time segment-level. 
     The segment FC  341  calculates an impression frequency per audience member, also referred to herein as a frequency, for each demographic group using the redistributed impressions and the redistributed audience counts at the time segment-level. 
     The segment DFG  342  calculates a ratio of duplicated audience counts to total audience counts across all platforms y, referred to herein as a deduplication factor, for each demographic group using the redistributed audience counts at the time segment-level. 
     The episode FC  343  calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the episode-level. 
     The episode DFG  344  calculates a deduplication factor for each demographic group using the redistributed audience counts at the episode-level. 
     The asset FC  345  calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the asset-level. 
     The asset DFG  346  calculates a deduplication factor for each demographic group using the redistributed audience counts at the asset-level. 
     The sub-brand FC  347  calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the sub-brand level. 
     The sub-brand DFG  348  calculates a deduplication factor for each demographic group using the redistributed audience counts at the sub-brand level. 
     The brand FC  349  calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the brand-level. 
     The brand DFG  350  calculates a deduplication factor for each demographic group using the redistributed audience counts at the brand-level. 
     The CP-FC  351  calculates a frequency for each demographic group using the redistributed audience counts at a cross-publisher level. As described in more detail below, the demographic distributor  318  determines a distribution of impression information (e.g., impressions, duration units, and/or audience counts) at the time segment-level and sums the distributed impression information at the time segment-level, respectively, across all brands b to generate impression information distributed at the cross-publisher level. 
     The CP-DFG  352  calculates a deduplication factor for each demographic group using the redistributed audience counts at the cross-publisher level. 
     The example matrix generator  322  generates and/or calculates misattribution correction matrices based on the survey calibration data. In examples disclosed herein, the matrix generator  322  creates a misattribution correction matrix for each combination of device type x and brand b. The matrix generator  322  receives survey calibration data from the calibration data collector  300  and calculates probabilities that: (1) a person of a recognized demographic group i is identified by the database proprietor  116  as a viewer of media, and (2) a person in an actual viewer demographic group j is an actual viewer, which is configured using the survey calibration data. Additional examples that may be used to implement the misattribution correction matrix generation process are disclosed in U.S. Patent Publication No. 2015/0262207 to Rao et al. The example matrix generator  322  includes an example matrix converter  353 . 
     The example matrix converter  353  converts the misattribution correction matrices to impression counts matrices. In examples disclosed herein, the matrix converter  353  utilizes a conversion formula to convert a probability of each cell in the misattribution correction matrices to an impressions count probability. In examples disclosed herein, the impression counts matrices are used to correct and/or calibrate impression information and to generate ratings information. Example implementations of the matrix converter  353  are described below in connection with  FIG.  9   . 
     The example E-S adjuster  324  adjusts impression counts and durations at the episode-level and the time-segment level. In examples disclosed herein, the example E-S adjuster  324  receives, from the demographic distributor  318 , and applies redistributed impression counts and redistributed durations at the episode-level and the time-segment level, respectively, to a corresponding impression counts matrix to generate misattribution adjusted impression counts and misattribution adjusted durations to compensate for device sharing. The E-S adjuster  324  compensates for database proprietor non-coverage error by applying coverage vectors to the misattribution impression counts and durations to generate coverage adjusted impression and durations. As used herein, coverage vectors reflect portions of impressions and/or durations attributable to persons by the database proprietor  116 . In examples disclosed herein, the E-S adjuster  324  scales the coverage adjusted impression counts and durations by applying scaling factors. The E-S adjuster  324  includes an impressions sharing adjuster  354 , a duration sharing adjuster  355 , a coverage vector calculator  356 , an impressions coverage adjuster  357 , a duration coverage adjuster  358 , an impression scaling factor generator  359 , a duration scaling factor generator  360 , an impressions scaler  361 , and a duration scaler  362 . Example implementations of the E-S adjuster  324  are described below in connection with  FIG.  17   . 
     The impressions sharing adjuster  354  receives impression counts matrices from the matrix converter  353  and redistributed impressions from the demographic distributor  318 . In examples disclosed herein, the impressions sharing adjuster  354  multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the redistributed impressions by a corresponding impression counts matrix to determine misattribution adjusted impressions. 
     The duration sharing adjuster  355  receives impression counts matrices from the matrix converter  353  and redistributed durations from the demographic distributor  318 . In examples disclosed herein, the duration sharing adjuster  355  multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the redistributed durations by the corresponding impression counts matrix to determine misattribution adjusted durations. 
     The coverage vector calculator  356  calculates coverage adjustment vectors for each brand b and device type x. In examples disclosed herein, the coverage vector calculator  356  receives survey calibration data from the calibration data collector  300  and determines a number or portion of persons (e.g., respondents to a survey) in a demographic group that indicate they would be recognized by the database proprietor  116  when using a particular device type x, as a proportion of the persons in the demographic group that own and have access to that particular device type x. The coverage vector calculator  356  calculates coverage adjustment vectors by determining a total number of respondents in a demographic group with access to a device of the device type x and dividing by a covered portion of respondents in the demographic group for the device type x based on the survey calibration data. 
     The impressions coverage adjuster  357  receives the misattribution adjusted impressions from the impressions sharing adjuster  354  and the coverage vectors from the coverage vector calculator  356 . In examples disclosed herein, the impressions coverage adjuster  357  multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the misattribution adjusted impressions by a corresponding coverage vector to generate coverage adjusted impression counts. 
     The duration coverage adjuster  358  receives the misattribution adjusted durations from the duration sharing adjuster  355  and the coverage vectors from the coverage vector calculator  356 . In examples disclosed herein, the duration coverage adjuster  358  multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the misattribution adjusted durations by the corresponding coverage vector to generate coverage adjusted durations. 
     The impression scaling factor generator  359  obtains and/or receives the aggregated numbers of impressions from the monitored website (e.g., the web site census counts) at the episode-level and the time-segment level, respectively. In examples disclosed herein, the impression scaling factor generator  359  determines impression scaling factors for each demographic group, at the episode-level and the time-segment level, respectively, by dividing the aggregated numbers of impressions by a sum of the coverage adjusted impressions across all demographic groups d. 
     The duration scaling factor generator  360  obtains and/or receives the aggregated numbers of duration units from the monitored website (e.g., the web site census counts) at the episode-level and the time-segment level, respectively. In examples disclosed herein, the duration scaling factor generator  360  determines duration scaling factors for each demographic group, at the episode-level and the time-segment level, respectively, by dividing the aggregated numbers of duration units by a sum of the coverage adjusted durations across all demographic groups d. 
     The impressions scaler  361  receives the impression scaling factors from the impression scaling factor generator  359  and multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the coverage adjusted impressions by a corresponding impression scaling factor to generate scaled impressions. 
     The duration scaler  362  receives the duration scaling factors from the duration scaling factor generator  360  and multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the coverage adjusted durations by a corresponding duration scaling factor to generate scaled durations. 
     The census imputer  326  further adjusts impression counts and durations at the asset-level, the episode-level, and the time-segment level. In examples disclosed herein, the census imputer  326  receives scaled impressions and durations from the E-S adjuster  324  and distributes the scaled impressions and durations at the asset-level. The census imputer  326  receives, from the monitored website, and imputes the aggregated numbers of impressions and the aggregated numbers of duration units. In examples disclosed herein the census imputer  326  calculates scaled and adjusted impressions and durations based on the imputation. The census imputer  326  includes an example impressions adder  363 , an example duration adder  364 , an example impressions imputation adder  365 , an example duration imputation adder  366 , an example impressions distribution deriver  367 , an example duration distribution deriver  368 , an example impressions imputation director  369 , an example duration imputation director  370 , an example impressions imputation calculator  371 , and an example duration imputation calculator  372 . Example implementations of the census imputer  326  are described below in connection with  FIG.  11   . 
     The impressions adder  363  receives the scaled impressions at the episode-level and the time-segment level, respectively, from the E-S adjuster  324  and sums, for each demographic group, the scaled impressions across all episodes e and time-segments i, respectively, to generate asset-level adjusted impressions. 
     The duration adder  363  receives the scaled durations at the episode-level and the time-segment level, respectively, from the E-S adjuster  324  and sums, for each demographic group, the scaled durations across all episode e and time-segments i, respectively, to generate asset-level adjusted durations. 
     The impressions imputation adder  365  receives the asset-level adjusted impressions from the impressions adder  363  and determines sums of the asset-level adjusted impressions across all demographic groups d. 
     The duration imputation adder  366  receives the asset-level adjusted durations from the duration adder  363  and determines sums of the asset-level adjusted durations across all of the demographic groups d. 
     The impressions distribution deriver  367  receives the asset-level adjusted impressions from the impressions adder  363  and the sums of the asset-level adjusted impressions across all demographic groups d from the impressions imputation adder  365 . In examples disclosed herein, the impressions distribution deriver  367  determines a ratio, for each demographic group d at the episode-level e and the time-segment level i, respectively, of the asset-level adjusted impressions to the sum of the asset-level adjusted impressions across all demographic groups d to generate adjusted impression demographic distributions. 
     The duration distribution deriver  368  receives the asset-level adjusted durations from the duration adder  364  and the sums of the asset-level adjusted durations across all demographic groups d from the duration imputation adder  366 . In examples disclosed herein, the duration distribution deriver  368  determines a ratio, for each demographic group d at the episode-level e and the time-segment level i, respectively, of the asset-level adjusted durations to the sum of the asset-level adjusted durations across all demographic groups d to generate adjusted duration demographic distributions. 
     The impressions imputation director  369  obtains site census impressions (e.g., the aggregated numbers of impressions received from the monitored website) and determines whether, at the episode-level e and the time-segment level i, respectively, the site census impressions are logged and the impressions from the database proprietor  116  are not logged. In examples disclosed herein, the impressions imputation director  369  imputes the site census impressions by applying, for each demographic group d at the episode-level e and the time-segment level i, respectively, the adjusted impression demographic distributions to the site census impressions to generate imputed impressions. 
     The duration imputation director  370  obtains site census durations (e.g., the aggregated numbers of duration units from the monitored website) and determines whether, at the episode level e and the time-segment level i, respectively, the site census durations are logged and the durations from the database proprietor are not logged. In examples disclosed herein, the duration imputation director  370  imputes the site census durations by applying, for each demographic group d at the episode-level e and the time-segment level i, respectively, the adjusted duration demographic distributions to the site census durations to generate imputed durations. 
     The impressions imputation calculator  371  receives the imputed impressions from the impressions imputation director  369  and the scaled impressions from the E-S adjuster  324  and determines adjusted and scaled impressions by adding, for each demographic group d at the episode-level e and the segment level i, respectively, the imputed impressions to the scaled impressions. 
     The duration imputation calculator  372  receives the imputed durations from the duration imputation director  370  and the scaled durations from the E-S adjuster  324  and determines adjusted and scaled durations by adding, for each demographic group d at the episode-level e and the segment level i, respectively, the imputed durations to the scaled durations. 
     The metrics generator  328  estimates impression information counts (e.g., estimated impression counts, estimated durations, and estimated audience counts), for each combination of demographic group d and platform y, at the time-segment level i, the episode-level e, the asset-level p, the sub-brand level s, and the brand-level b. In examples disclosed herein, the metrics generator  328  receives adjusted and scaled impressions and durations from the census imputer  326 . The metrics generator  328  also receives frequencies and deduplication factors from the distributed demographic data calculator  320 . In examples disclosed herein, estimated impression counts, referred to herein as time-segment level estimates (e.g., time-segment level impression estimates, time-segment level duration estimates, and time-segment level audience estimates), are generated at the time-segment level i by the metrics generator  328  and estimated impression counts, referred to herein as episode-level estimates (e.g., episode-level impression estimates, episode-level duration estimates, and episode-level audience estimates), are generated at the episode level e by the metrics generator  328 . Additionally, the estimated impression counts rolled up from the time-segment level to the asset-level are referred to herein as first-asset level estimates (e.g., first asset-level impression estimates, first asset-level duration estimates, and first-asset level audience estimates) whereas the estimated impression counts rolled up from the episode-level to the asset-level are referred to herein as second-asset level estimates (e.g., second asset-level impression estimates, second asset-level duration estimates, and second asset-level duration estimates). Example implementations of the metrics generator  328  are described below in connection with  FIGS.  12  and  13   . 
     The segment modifier  330  adjusts ones of the estimated impression counts for each demographic group d at the time-segment level i. In examples disclosed herein, the segment modifier  330  receives ones of the time-segment level estimates from the metrics generator  328 . The segment modifier  330  calculates impression ratios and duration ratios at a sub-segment level i s  to adjust ones of the time-segment level estimates. The example segment modifier  330  includes an example impression sub-segment adder  373 , an example duration sub-segment adder  374 , an example impression ratio calculator  375 , an example duration ratio calculator  376 , an example impression metrics adjuster  377 , and an example duration metrics adjuster  378 . Example implementation of the segment modifier  330  are described below in connection with  FIG.  21   . 
     The impression sub-segment adder  373  receives time-segment level impression estimates from the metrics generator  328 . In examples disclosed herein, the impression sub-segment adder  373  identifies or tracks, for each time-segment level impression estimate, durational segments (e.g., D beacons) representative of impressions identified by the AME  114  and the DP  116 . The impression sub-segment adder  373  may identify, for each time-segment level impression estimate determined for platform y, brand b, sub-brand s, asset p, time-segment z, and demographic d, sub-segments of the corresponding D beacons. For example, if the time-segment level impression estimate is partially derived from a D beacon of D_2_01010, the sub-segment adder  373  identifies the sub-segments (e.g., 01010) and determines a quantity of total identified sub-segments in the D beacon (e.g., a total of 5 sub-segments). As such, the impression sub-segment adder  373  determines a sum of sub-segments i s  of the media  206  for each of the time-segment level impression estimates. In this manner, the impression sub-segment adder  373  may calculate a sum of sub-segments for all D beacons corresponding to the nine-segment level impression estimates. 
     The duration sub-segment adder  374  receives time-segment level duration estimates from the metrics generator  328 . In examples disclosed herein, the duration sub-segment adder  374  identifies or tracks, for each time-segment level duration estimate, the durational segments (e.g., the D beacons). The duration sub-segment adder  374  may identify, for each time-segment level duration estimate determined for platform y, brand b, sub-brand s, asset p, time segment z, and demographic d, durations of the corresponding D beacons. For example, if the time-segment level duration estimate is partially derived from the D beacon of the aforementioned example (e.g., D_2_01010), the duration sub-segment adder  374  identifies the durations of the sub-segments (e.g., each sub-segment is equal to a duration of one minute) and determines a quantity of total identified sub-segment durations in the D beacon (e.g., a total duration of 5 minutes). As such, the duration sub-segment adder  374  determines a sum of sub-segment durations of the media  206  for each of the time-segment level duration estimates. In this manner, the duration sub-segment adder  374  may calculate a sum of sub-segment durations for all D beacons corresponding to the time-segment level duration estimates. 
     The impression ratio calculator  375  receives time-segment level impression estimates from the metrics generator  328  and the sums of the sub-segments is for each time-segment z from the impression sub-segment adder  373 . In examples disclosed herein, the impression ratio calculator  375  calculates an impression ratio for each combination of platform y, brand b, sub-brand s, asset p, time segment z, and demographic d, by dividing a quantity of time sub-segments presented (e.g., credited as impressions) in the time-segment level impression estimates by the sum of the sub-segments i s , for the demographic group d and the time-segment z. For example, if time-segment level impression estimate is partially derived from the D beacon, D_2_01010, the quantity of time sub-segments presented in the time-segment level impression estimate is 2 (e.g., of the 5 sub-segments, the 2nd and 4th sub-segments of the media  206  were credited as being presented). Thus, the ratio of the time-segment level impression estimate (e.g., 2) and the sum of the sub-segments is for the time-segment z (e.g., 5) is the impression ratio (e.g., 2/5). In examples disclosed herein, the impression ratio calculator  375  may determine an impression ratio for each corresponding D beacon and sum the impression ratios across all D beacons. 
     The duration ratio calculator  376  receives time-segment level duration estimates from the metrics generator  328  and the sums of the sub-segment durations for each time-segment z from the duration sub-segment adder  374 . In examples disclosed herein, the duration ratio calculator  376  calculates a duration ratio for each combination of platform y, brand b, sub-brand s, asset p, time segment z, and demographic d, by dividing a quantity of presented time sub-segment durations (e.g., credited as durations) of the time-segment level duration estimate by the sum of the sub-segment durations for the demographic group d and the time-segment z. For example, if a time-segment level duration estimate is partially derived from the D beacon, D_2_01010, the quantity of presented time sub-segment durations in the time-segment level duration estimate is 2 minutes (e.g., of the 5 sub-segments, each one minute in duration, the 2 nd  and 4 th  sub-segments of the media  206  were credited as being presented). Thus, the ratio of the time-segment level duration estimate (e.g., 2 minutes) and the sum of the sub-segment durations for the time-segment z (e.g., 5 minutes) is the duration ratio (e.g., 2 minute/5 minutes=2/5). In examples disclosed herein, the duration ratio calculator  376  may determine a duration ratio for each corresponding D beacon and sum the impression ratios across all D beacons. 
     The impression metrics adjuster  377  receives first asset-level impressions from the metrics generator  328  and the impression ratios from the impression ratio calculator  375 . In examples disclosed herein, the impression metrics adjuster  377  adjusts the first asset-level impressions by applying (e.g., multiplying), for each platform y, brand b, sub-brand s, asset p, segment z, and demographic d, the impression ratio of the segment z and the demographic d to the first-asset level impressions of the demographic d. The resulting product produces adjusted time-segment level impression estimates. 
     The duration metrics adjuster  378  receives first asset-level durations from the metrics generator  328  and the duration ratios from the duration ratio calculator  376 . In examples disclosed herein, the durations metrics adjuster  378  adjusts the first asset-level durations by applying (e.g., multiplying), for each platform y, brand b, sub-brand s, asset p, segment z, and demographic d, the duration ratio of the segment z and the demographic d to the first-asset level durations of the demographic d. The resulting product produces adjusted time-segment level duration estimates. 
     The example brand/cross-platform adjuster  332  adjusts the adjusted time-segment level impression and duration estimates at the brand-level b. In examples disclosed herein, the brand/cross-platform adjuster  332  receives time segment-level estimates from the segment modifier  330  and frequencies and deduplication factors from the distributed demographic data calculator  320 . The brand/cross-platform adjuster  332  includes an example impressions adjuster  379 , an example duration adjuster  380 , an example audience adjuster  381 , an example impression total digital (TD) calculator  382 , an example duration total digital (TD) calculator  383 , and an example audience total digital (TD) calculator  384 . Example implementation of the brand/cross-platform adjuster  332  is described below in connection with  FIG.  22   . 
     The impressions adjuster  379  receives the adjusted time-segment-level impression estimates from the segment modifier  330  and calculates, for each demographic group d, adjusted brand-level impression estimates (e.g., brand-level impression counts) by summing the adjusted time segment-level impression estimates across all assets p within sub-brands s. Additionally or alternatively, the example impressions adjuster  379  calculates, for each demographic group d, cross publisher adjusted brand-level impression estimates (e.g., cross-publisher adjusted brand-level impression counts) by summing the adjusted brand-level impression estimates across all brands b (e.g., across all network affiliates providing a segment z of the media  206 ). 
     The duration adjuster  380  receives the adjusted time segment-level duration estimates (e.g., the adjusted time segment-level durations) from the segment modifier  330  and calculates, for each demographic group d, adjusted-brand level duration estimates by summing the time-segment level duration estimates across all assets p within sub-brands s. Additionally or alternatively, the duration adjuster  380  calculates, for each demographic group d, cross publisher adjusted-brand level durations by summing the adjusted brand-level durations across all brands b. 
     The audience adjuster  381  receives the adjusted brand-level impression estimates from the impression adjuster  379  and the frequency at the brand-level b, referred to herein as brand-level frequency, from the brand FC  349 . In examples disclosed herein, the audience adjuster  381  calculates adjusted brand-level audience estimates by dividing, for each demographic group d, the adjusted brand-level impression estimate by the brand-level frequency. Additionally or alternatively, the example audience adjuster receives the frequency at the cross publisher level, referred to herein as cross publisher-level frequency, from the CP-FC  351 . In examples disclosed herein, the audience adjuster  381  calculates cross publisher adjusted brand-level audience estimates (e.g., cross publisher adjusted brand-level audience counts) by dividing, for each demographic group d, the cross publisher adjusted brand-level impression count by the cross publisher-level frequency. 
     The impressions TD calculator  382  receives the adjusted brand-level impression estimates from the impressions adjuster  379  and determines total digital brand-level impression estimates, by adding, for each demographic group d, adjusted brand-level impression counts presented at a time segment z via a non-mobile (e.g., desktop) computing platform to adjusted brand-level impression counts presented at the time segment z via a mobile computing platform. Additionally or alternatively, the impressions TD calculator  382  determines cross publisher total digital brand-level impression estimates (e.g., cross publisher total digital brand-level impression counts), by adding, for each demographic group d, cross publisher adjusted brand-level impression estimates presented at a time segment z via a non-mobile (e.g., desktop) computing platform to cross publisher adjusted brand-level impression estimates presented at the time segment z via a mobile computing platform. 
     The duration TD calculator  383  receives the adjusted brand-level durations from the duration adjuster  380  and determines total digital brand-level durations, by adding, for each demographic group d, adjusted brand-level durations presented at a time segment z via a non-mobile (e.g., desktop) computing platform to adjusted brand-level impression durations presented at the time segment z via a mobile computing platform. Additionally or alternatively, the duration TD calculator  383  determines cross publisher total digital brand-level durations, by adding, for each demographic group d, cross publisher adjusted brand-level duration estimates presented at a time segment z via a non-mobile (e.g., desktop) computing platform to cross publisher adjusted brand-level duration estimates presented at the time segment z via a mobile computing platform. 
     The audience TD calculator  384  receives the deduplication factors at the brand level b, referred to herein as brand-level deduplication factors, from the brand DFG  350 . The audience TD calculator  384  also receives the adjusted brand-level audience counts from the audience adjuster  381 . In examples disclosed herein, the TD calculator  384  determines total digital brand-level audience counts by adding, for each demographic group d, adjusted brand-level audience counts presented at a time segment z via a non-mobile (e.g., desktop) computing platform to adjusted brand-level audience counts presented at the time segment z via a mobile computing platform and multiplying the sum by a corresponding brand-level deduplication factor. 
     In some examples, the audience TD calculator  384  receives the deduplication factors at the cross publisher level, referred to herein as cross publisher-level deduplication factors, from the CP-DFG  352 . The example audience TD calculator  384  determines cross publisher total digital brand-level audience estimates by adding, for each demographic group d, cross publisher adjusted brand-level audience estimates presented at a time segment z via a non-mobile (e.g., desktop) computing platform to cross publisher adjusted brand-level audience estimates presented at the time segment z via a mobile computing platform and multiplying the sum by a corresponding cross publisher-level deduplication factor. 
     The brand/cross-platform adjuster  332  also adjusts the adjusted time-segment level impression and duration estimates at the cross-publisher level. In examples disclosed herein, the brand/cross-platform adjuster  332  determines the adjusted brand-level impression counts, and receives the frequencies and deduplication factors from the distributed demographic data calculator  320 . 
     The content calculator  336  receives redistributed impression counts, duration units, and/or audience counts organized by content type c (e.g., text content t, video content v, or both text and video content w) from the demographic distributor  318 . The example content calculator  336  also receives adjusted brand-level impression estimates from the impressions adjuster  379 . In examples disclosed herein, the content calculator  336  generates text and video (T/V) deduplication factors based on the audience counts organized by content type c. In examples disclosed herein, the content calculator  336  determines corrected audience counts (e.g., total content audience counts) based on the (T/V) deduplication factors and the adjusted brand-level audience estimates. The corrected audience counts indicate a quantity of audience members, of a demographic group d, that were presented text content t and/or video content v for a particular segment z of the media  206  of  FIG.  2   . In examples disclosed herein, the corrected audience counts are based on a quantity of audience members that accessed text content t, referred to herein as text audience counts, and a quantity of audience members that accessed video content v, referred to herein as video audience counts. The content calculator  336  includes an example text and video (T/V) deduplication factor generator (DFG)  391 , an example text and video (T/V) audience calculator  392 , an example total digital (TD) deduplication factor generator (DFG)  393 , and an example total digital (TD) audience calculator  394 . Example implementation of the content calculator adjuster  336  is described below in connection with  FIG.  24   . 
     The T/V DFG  391  receives audience counts redistributed by text content t, video content v, and both text and video content w from the demographic distributor  318 . In examples disclosed herein, the T/V DFG  391  calculates, for each demographic d and time segment z, a T/V deduplication factor by dividing a redistributed audience count indicative of both text and video w (e.g., a duplicated content audience count) by a sum of a redistributed audience count indicative of text t (e.g., a text redistributed audience count) and a redistributed audience count indicative of video v (e.g., a video redistributed audience count). 
     The T/V audience calculator  392  receives adjusted brand-level impression estimates from the impressions adjuster  379 . The T/V audience calculator  392  also generates T/V deduplication factors using the T/V DFG  391 . In examples disclosed herein, the T/V audience calculator  392  calculates a text frequency by dividing, for each demographic group d, an impression count redistributed by text t by an audience count redistributed by text t. The T/V audience calculator  392  also calculates a video frequency by dividing, for each demographic group d, an impression count redistributed by video v by an audience count redistributed by video v. In examples disclosed herein the T/V audience calculator  392  determines, for each demographic d, a text audience count by dividing an adjusted brand-level impression estimate by the text frequency. The T/V audience calculator  392  further determines, for each demographic d, a video audience count by dividing the adjusted brand-level impression estimate by the video frequency. In examples disclosed herein, the T/V audience calculator  392  calculates a corrected audience count (e.g., a total content audience count), for each demographic d, by applying a T/V deduplication factor to a sum of the text audience count and the video audience count. 
     The TD DFG  393  receives audience counts redistributed by text content t, video content v, and both text and video content w from the demographic distributor  318 . In examples disclosed herein, the TD DFG  393  determines, for each demographic d, a total digital (TD) text and video (T/V) deduplication factor by dividing a duplicated platform audience count (e.g., an audience count, based on all audience counts redistributed by content type c, credited to both a non-mobile computing platform and a mobile computing platform) by a sum of a non-mobile computing platform audience count (e.g., an audience count, based on all audience counts redistributed by content type c, credited to the non-mobile computing platform) and a mobile computing platform audience count (e.g., an audience count, based on all audience counts redistributed by content type c, credited to the mobile computing platform). 
     The TD audience calculator  394  receives the TD T/V deduplication factors from the TD DFG  393 . The TD audience calculator  394  also accesses and/or receives the adjusted brand-level impression estimates from the impressions adjuster  379 . In examples disclosed herein, the TD audience calculator  394  calculates a total digital text frequency by dividing, for each demographic group d, an impression count redistributed by text t across all platforms y by an audience count redistributed by text t across all platforms y. The TD audience calculator  394  also calculates a total digital video frequency by dividing, for each demographic group d, an impression count redistributed by video v across all platforms y by an audience count redistributed by video v across all platforms y. In examples disclosed herein the TD audience calculator  394  determines, for each demographic d, a total digital text audience count by dividing an adjusted brand-level impression estimate by the total digital text frequency. The TD audience calculator  394  further determines, for each demographic d, a total digital video audience count by dividing the adjusted brand-level impression estimate by the total digital video frequency. In examples disclosed herein, the TD audience calculator  394  calculates a total digital corrected audience count (e.g., a total content audience count), for each demographic d, by applying a TD T/V deduplication factor to a sum of the total digital text audience count and the total digital video audience count. 
     The ratings data generator  338  generates ratings information for media of interest for the demographic groups d using corrected and/or adjusted impression and/or audience information (e.g., the corrected audience counts, the total content audience counts, etc.). 
     While an example manner of implementing the audience data generator  120   a - b  of  FIGS.  1  and/or  2    is illustrated in  FIG.  3   , one or more of the elements, processes and/or devices illustrated in  FIG.  3    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example calibration data collector  300 , the example platform data manager  302 , the example device data manager  304 , the example segment data manager  306 , the example episode data manager  308 , the example asset data manager  310 , the example sub-brand data manager  312 , the example brand data manager  314 , the example database proprietor (DP) data manager  316 , the example demographic distributor  318 , the example distributed demographic data calculator  320 , the example matrix generator  322 , the example episode-segment (E-S) adjuster  324 , the example census imputer  326 , the example metrics generator  328 , the example segment modifier  330 , the example brand/cross-platform adjuster  332 , the example content calculator  336 , the example ratings data generator  338 , the example duration-per-impression (DPI) calculator  340 , the example segment frequency calculator (FC)  341 , the example segment deduplication factor generator (DFG)  342 , the example episode frequency calculator (FC)  343 , the example episode deduplication factor generator (DFG)  344 , the example asset frequency calculator (FC)  345 , the example asset deduplication factor generator (DFG)  346 , the example sub-brand frequency calculator (FC)  347 , the example sub-brand deduplication factor generator (DFG)  348 , the example brand frequency calculator (FC)  349 , the example brand deduplication factor generator (DFG)  350 , the example cross publisher frequency calculator (CP-FC)  351 , the example cross publisher deduplication factor generator (CP-DFG)  352 , the example matrix converter  353 , the example impressions sharing adjuster  354 , the example duration sharing adjuster  355 , the example coverage vector calculator  356 , the example impressions coverage adjuster  357 , the example duration coverage adjuster  358 , the example impression scaling factor generator  359 , the example duration scaling factor generator  360 , the example impressions scaler  361 , the example duration scaler  362 , the example impressions adder  363 , the example duration adder  364 , the example impressions imputation adder  365 , the example duration imputation adder  366 , the example impressions distribution deriver  367 , the example duration distribution deriver  368 , the example impressions imputation director  369 , the example duration imputation director  370 , the example impressions imputation calculator  371 , the example duration imputation calculator  372 , the example impression sub-segment adder  373 , the example duration sub-segment adder  374 , the example impression ratio calculator  375 , the example duration ratio calculator  376 , the example impression metrics adjuster  377 , the example duration metrics adjuster  378 , the example impressions adjuster  379 , the example duration adjuster  380 , the example audience adjuster  381 , the example impressions total digital (TD) calculator  382 , the example duration total digital (TD) calculator  383 , the example audience total digital (TD) calculator  384 , the example text and video (T/V) deduplication factor generator (DFG)  391 , the example text and video (T/V) audience calculator  392 , the example total digital (TD) deduplication factor generator  393 , the example total digital (TD) audience calculator  394  and/or, more generally, the example audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example calibration data collector  300 , the example platform data manager  302 , the example device data manager  304 , the example segment data manager  306 , the example episode data manager  308 , the example asset data manager  310 , the example sub-brand data manager  312 , the example brand data manager  314 , the example database proprietor (DP) data manager  316 , the example demographic distributor  318 , the example distributed demographic data calculator  320 , the example matrix generator  322 , the example episode-segment (E-S) adjuster  324 , the example census imputer  326 , the example metrics generator  328 , the example segment modifier  330 , the example brand/cross-platform adjuster  332 , the example content calculator  336 , the example ratings data generator  338 , the example duration-per-impression (DPI) calculator  340 , the example segment frequency calculator (FC)  341 , the example segment deduplication factor generator (DFG)  342 , the example episode frequency calculator (FC)  343 , the example episode deduplication factor generator (DFG)  344 , the example asset frequency calculator (FC)  345 , the example asset deduplication factor generator (DFG)  346 , the example sub-brand frequency calculator (FC)  347 , the example sub-brand deduplication factor generator (DFG)  348 , the example brand frequency calculator (FC)  349 , the example brand deduplication factor generator (DFG)  350 , the example cross publisher frequency calculator (CP-FC)  351 , the example cross publisher deduplication factor generator (CP-DFG)  352 , the example matrix converter  353 , the example impressions sharing adjuster  354 , the example duration sharing adjuster  355 , the example coverage vector calculator  356 , the example impressions coverage adjuster  357 , the example duration coverage adjuster  358 , the example impression scaling factor generator  359 , the example duration scaling factor generator  360 , the example impressions scaler  361 , the example duration scaler  362 , the example impressions adder  363 , the example duration adder  364 , the example impressions imputation adder  365 , the example duration imputation adder  366 , the example impressions distribution deriver  367 , the example duration distribution deriver  368 , the example impressions imputation director  369 , the example duration imputation director  370 , the example impressions imputation calculator  371 , the example duration imputation calculator  372 , the example impression sub-segment adder  373 , the example duration sub-segment adder  374 , the example impression ratio calculator  375 , the example duration ratio calculator  376 , the example impression metrics adjuster  377 , the example duration metrics adjuster  378 , the example impressions adjuster  379 , the example duration adjuster  380 , the example audience adjuster  381 , the example impressions total digital (TD) calculator  382 , the example duration total digital (TD) calculator  383 , the example audience total digital (TD) calculator  384 , the example text and video (T/V) deduplication factor generator (DFG)  391 , the example text and video (T/V) audience calculator  392 , the example total digital (TD) deduplication factor generator  393 , the example total digital (TD) audience calculator  394 , and/or, more generally, the example audience data generator  120  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example calibration data collector  300 , the example platform data manager  302 , the example device data manager  304 , the example segment data manager  306 , the example episode data manager  308 , the example asset data manager  310 , the example sub-brand data manager  312 , the example brand data manager  314 , the example database proprietor (DP) data manager  316 , the example demographic distributor  318 , the example distributed demographic data calculator  320 , the example matrix generator  322 , the example episode-segment (E-S) adjuster  324 , the example census imputer  326 , the example metrics generator  328 , the example segment modifier  330 , the example brand/cross-platform adjuster  332 , the example content calculator  336 , the example ratings data generator  338 , the example duration-per-impression (DPI) calculator  340 , the example segment frequency calculator (FC)  341 , the example segment deduplication factor generator (DFG)  342 , the example episode frequency calculator (FC)  343 , the example episode deduplication factor generator (DFG)  344 , the example asset frequency calculator (FC)  345 , the example asset deduplication factor generator (DFG)  346 , the example sub-brand frequency calculator (FC)  347 , the example sub-brand deduplication factor generator (DFG)  348 , the example brand frequency calculator (FC)  349 , the example brand deduplication factor generator (DFG)  350 , the example cross publisher frequency calculator (CP-FC)  351 , the example cross publisher deduplication factor generator (CP-DFG)  352 , the example matrix converter  353 , the example impressions sharing adjuster  354 , the example duration sharing adjuster  355 , the example coverage vector calculator  356 , the example impressions coverage adjuster  357 , the example duration coverage adjuster  358 , the example impression scaling factor generator  359 , the example duration scaling factor generator  360 , the example impressions scaler  361 , the example duration scaler  362 , the example impressions adder  363 , the example duration adder  364 , the example impressions imputation adder  365 , the example duration imputation adder  366 , the example impressions distribution deriver  367 , the example duration distribution deriver  368 , the example impressions imputation director  369 , the example duration imputation director  370 , the example impressions imputation calculator  371 , the example duration imputation calculator  372 , the example impression sub-segment adder  373 , the example duration sub-segment adder  374 , the example impression ratio calculator  375 , the example duration ratio calculator  376 , the example impression metrics adjuster  377 , the example duration metrics adjuster  378 , the example impressions adjuster  379 , the example duration adjuster  380 , the example audience adjuster  381 , the example impressions total digital (TD) calculator  382 , the example duration total digital (TD) calculator  383 , the example audience total digital (TD) calculator  384 , the example text and video (T/V) deduplication factor generator (DFG)  391 , the example text and video (T/V) audience calculator  392 , the example total digital (TD) deduplication factor generator  393 , and/or the example total digital (TD) audience calculator  394  and/or, more generally, the example audience data generator  120  is/are hereby expressly defined to include a tangible computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing the software and/or firmware. Further still, the example audience data generator  120  of  FIGS.  1 ,  2  and/or  3    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  3   , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
     Flowcharts representative of example machine readable instructions for implementing the audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  are shown in  FIGS.  4 - 7  and  9 - 16   . In this example, the machine readable instructions comprise a program for execution by a processor such as the processor  1712  shown in the example processor platform  1700  discussed below in connection with  FIG.  17   . The program(s) may be embodied in software stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor  1712 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  1712  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated in  FIGS.  4 - 7  and  9 - 16   , many other methods of implementing the example audience data generator  120  may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. 
     As mentioned above, the example processes of  FIGS.  4 - 7  and  9 - 16    may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a tangible computer readable storage medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, a random-access memory (RAM) and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, “tangible computer readable storage medium” and “tangible machine readable storage medium” are used interchangeably. Additionally or alternatively, the example processes of  FIGS.  4 - 7  and  9 - 16    may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” is open ended. 
       FIG.  4    is a flowchart representative of example machine readable instructions  400  which may be executed to implement the audience data generator  120  of  FIGS.  1 - 3    to determine rating data from population sample data having incomplete demographic classifications. 
     The example AME impressions collector  218  collects messages (e.g., the beacon requests  212 ,  226   b ) indicating impressions of a video delivered to devices (e.g., the client device  102 ) via the Internet (block  402 ). 
     The example DP impressions collector  232  receives, from the DP  116 , a first audience count describing first numbers of impressions corresponding to the messages attributed to the video for a demographic group (Block  404 ). The example DP impressions collector  232  receives, from the DP  116 , a second audience count describing second numbers of impressions corresponding to the messages attributed to text content for the demographic group (Block  406 ). The example DP impressions collector  232  receives, from the DP  116 , a third audience count describing third numbers of impressions corresponding to the messages attributed to the video and the text content for the demographic group (Block  408 ). 
     The example audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  (e.g., via the content calculator  336  of  FIG.  3   ) determines a corrected audience count of the video for the demographic group. The example T/V calculator  392  estimates a fourth audience count for the demographic group (Block  410 ). The fourth audience count indicates a number of people who accessed the video. The example T/V calculator  392  also estimates a fifth audience count for the demographic group (Block  412 ). The fifth audience count indicates a number of people who accessed the text content. 
     The example audience data generator  120  (e.g., via the T/V DFG  391 ) calculates a deduplication factor for the demographic group using the first audience count, the second audience count, and the third audience count (Block  414 ). 
     The example audience data generator  120  (e.g., via the T/V audience calculator  392 ) applies the deduplication factor to a sum of the fourth audience count and the fifth audience count to determine the corrected audience count (Block  416 ). 
     The example audience data generator (e.g., via the ratings data generator  338 ) generates ratings information for the video based on subtracting the corrected audience count from the first audience count (Block  418 ). 
     The example instructions  400  of  FIG.  4    may then end. More detailed examples of implementing the instructions of  FIG.  4    are described below with reference to  FIGS.  4 - 7  and  9 - 16   . 
     Gender Redistribution at the Episode Level 
       FIG.  5    is a flowchart representative of example machine readable instructions  500  which may be executed to implement the audience data generator of  FIGS.  1 - 3    to perform demographic redistribution of database proprietor demographic data. The example instructions  500  of  FIG.  5    may be executed to perform demographic redistribution at any of multiple levels. Example levels include the hierarchical levels for which the audience data generator receives aggregate demographic data for impressions, audience counts, and/or duration from a database proprietor. For example, demographic redistribution may occur for impressions, audience counts, and/or duration attributed by the database proprietor to a combination of a platform, a device type, an age group, a time segment, an episode, an asset, a sub-brand, and/or a brand. The example demographic distributor  318  of  FIG.  3    may perform the instructions  500  of  FIG.  5    to redistribute impressions, durations, and/or unique audience members for which the DP  116  is unable to determine a gender at the example time segment level, the example episode level, the example asset level, the example sub-brand level, and/or the example brand level. The example instructions  500  are described below with respect to a redistribution of a gender group, but may be modified to redistribute DP demographic data according to any demographic quality. 
     In some examples, aggregated demographic information may not be collected for impressions, audience counts, and/or durations at some hierarchical levels. In such examples, the corresponding gender redistribution blocks may be omitted from the instructions  500 . 
     Table 1 illustrates an example demographic redistribution of database proprietor demographic data at the episode level. Table 2 illustrates an example demographic redistribution of database proprietor demographic data at the asset level. Table 3 illustrates an example demographic redistribution of demographic data at the sub-brand level. Table 4 illustrates an example demographic redistribution of database proprietor demographic data at the brand level. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Example redistribution of impressions, duration, and audience counts 
               
               
                 for which gender is unknown at the example episode level 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Impr (from DP, 
                 Gender 
                   
               
               
                   
                 Sub- 
                   
                   
                   
                 deduplicated across 
                 Impr 
                 Redistr 
               
               
                 Brand 
                 Brand  
                 Asset 
                 Episode 
                 Demo 
                 all segments) 
                 Distr 
                 Imprs 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 D1 = M12+ 
                 12,557 
                 59.6% 
                 13,970 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 D2 = F12+ 
                 8,496 
                 40.4% 
                 9,460 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 Unknown 
                 2,387 
                   
                   
               
               
                   
                   
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 Total 
                 23,340 
                   
                 23,340 
               
               
                   
               
               
                   
                   
                   
                   
                   
                 Dur (from DP, 
                 Gender 
                 Redistr 
               
               
                   
                 Sub-  
                   
                   
                   
                 deduplicated across 
                 Dur 
                 Dur 
               
               
                 Brand 
                 Brand 
                 Asset 
                 Episode 
                 Demo 
                 all segments)(min.)  
                 Distr 
                 (min.) 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 D1 = M12+ 
                 138,868 
                 60.4%  
                 153,307 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 D2 = F12+ 
                 91,004 
                 39.6%  
                 100,471 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 Unknown 
                 23,906 
                   
                   
               
               
                   
                   
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 Total 
                 253,778 
                   
                 253,778 
               
               
                   
               
               
                   
                   
                   
                   
                   
                 UA (from DP, 
                 Gender 
                   
               
               
                   
                 Sub- 
                   
                   
                   
                 deduplicated across 
                 UA 
                 Redistr 
               
               
                 Brand 
                 Brand  
                 Asset 
                 Episode 
                 Demo 
                 all segments) 
                 Distr 
                 UA 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 D1 = M12+ 
                 105 
                 54.7% 
                 116 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 D2 = F12+ 
                 87 
                 45.3% 
                 96 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 Unknown 
                 20 
                   
                   
               
               
                   
                   
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Ep1 
                 Total 
                 212 
                   
                 212 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Example redistribution of impressions, duration, and audience counts  
               
               
                 for which gender is unknown at the example asset level 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 Impr (from DP, 
                   
                   
               
               
                   
                   
                   
                   
                 deduplicated across 
                 GenderImpr 
                   
               
               
                 Brand 
                 Sub-Brand 
                 Asset 
                 Demo 
                 all segments) 
                 Distr 
                 Redistr Imprs 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 D1 = M12+ 
                 15,751 
                 56.8% 
                 17,107 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 D2 = F12+ 
                 11,987 
                 43.2%  
                 13,018 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Unknown 
                 2,387 
                   
                   
               
               
                   
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Total 
                 30,125 
                   
                 30,125 
               
               
                   
               
               
                   
                   
                   
                   
                 Dur (from DP, 
                   
                   
               
               
                   
                   
                   
                   
                 deduplicated across 
                 Gender Dur 
                 Redistr Dur 
               
               
                 Brand 
                 Sub-Brand 
                 Asset 
                 Demo 
                 all segments)(min.) 
                 Distr 
                 (min.) 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 D1 = M12+ 
                 175,312 
                 54.7%  
                 188,389 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 D2 = F12+ 
                 145,119 
                 45.3%  
                 155,948 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Unknown 
                 23,906 
                   
                   
               
               
                   
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Total 
                 344,337 
                   
                 334,337 
               
               
                   
               
               
                   
                   
                   
                   
                 UA (from DP, 
                   
                   
               
               
                   
                   
                   
                   
                 deduplicated across 
                 Gender UA 
                   
               
               
                 Brand 
                 Sub-Brand 
                 Asset 
                 Demo 
                 all segments) 
                 Distr 
                 Redistr UA 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 D1 = M12+ 
                 125 
                 38.5% 
                 133 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 D2 = F12+ 
                 200 
                 61.5% 
                 212 
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Unknown 
                 20 
                   
                   
               
               
                   
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 As1 
                 Total 
                 345 
                   
                 345 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 Example redistribution of impressions, duration, and audience counts  
               
               
                 for which gender is unknown at the example sub-brand level 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                 Impr (from DP, 
                   
                   
               
               
                   
                   
                   
                 deduplicated  
                 Gender  
                   
               
               
                   
                 Sub- 
                   
                 across all  
                 Impr 
                 Redistr 
               
               
                 Brand  
                 Brand 
                 Demo 
                 segments) 
                 Distr 
                 Imprs 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 D1 = M12+ 
                 21,345 
                 60% 
                 22,777 
               
               
                 Br1 
                 S-Br1 
                 D2 = F12+ 
                 16,789 
                 40% 
                 17,744 
               
               
                 Br1 
                 S-Br1 
                 Unknown 
                 2,387 
                   
                   
               
               
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 Total 
                 40,521 
                   
                 40,521 
               
               
                   
               
               
                   
                   
                   
                 Dur (from DP, 
                   
                   
               
               
                   
                   
                   
                 deduplicated  
                 Gender  
                 Redistr 
               
               
                   
                 Sub- 
                   
                 across all  
                 Dur 
                 Dur 
               
               
                 Brand 
                 Brand 
                 Demo 
                 segments)(min.) 
                 Distr 
                 (min.) 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 D1 = M12+ 
                 211,467 
                 51.7% 
                 223,826 
               
               
                 Br1 
                 S-Br1 
                 D2 = F12+ 
                 197,651 
                 48.3% 
                 209,198 
               
               
                 Br1 
                 S-Br1 
                 Unknown 
                 23,906 
                   
                   
               
               
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 Total 
                 433,024 
                   
                 433,024 
               
               
                   
               
               
                   
                   
                   
                 UA (from DP, 
                   
                   
               
               
                   
                   
                   
                 deduplicated  
                 Gender 
                   
               
               
                   
                 Sub- 
                   
                 across all  
                 UA 
                 Redistr 
               
               
                 Brand 
                 Brand 
                 Demo 
                 segments) 
                 Distr 
                 UA 
               
               
                   
               
               
                 Br1 
                 S-Br1 
                 D1 = M12+ 
                 250 
                 45.5% 
                 259 
               
               
                 Br1 
                 S-Br1 
                 D2 = F12+ 
                 300 
                 54.5% 
                 311 
               
               
                 Br1 
                 S-Br1 
                 Unknown 
                 20 
                   
                   
               
               
                   
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 S-Br1 
                 Total 
                 570 
                   
                 570 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Example redistribution of impressions, duration, and audience counts  
               
               
                 for which gender is unknown at the example brand level 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Impr (from DP, 
                   
                   
               
               
                   
                   
                 deduplicated  
                 Gender  
                   
               
               
                   
                   
                 across all  
                 Impr 
                 Redistr 
               
               
                 Brand  
                 Demo 
                 segments) 
                 Distr 
                 Imprs 
               
               
                   
               
               
                 Br1 
                 D1 = M12+ 
                 42,323 
                 66.0% 
                 43,898 
               
               
                 Br1 
                 D2 = F12+ 
                 21,789 
                 34.0% 
                 22,601 
               
               
                 Br1 
                 Unknown 
                 2,387 
                   
                   
               
               
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 Total 
                 66,499 
                   
                 66,499 
               
               
                   
               
               
                   
                   
                 Dur (from DP, 
                   
                   
               
               
                   
                   
                 deduplicated  
                 Gender  
                 Redistr 
               
               
                   
                   
                 across all  
                 Dur 
                 Dur 
               
               
                 Brand 
                 Demo 
                 segments)(min.) 
                 Distr 
                 (min.) 
               
               
                   
               
               
                 Br1 
                 D1 = M12+ 
                 292,178 
                 57.4% 
                 305,900 
               
               
                 Br1 
                 D2 = F12+ 
                 216,782 
                 42.6% 
                 226,966 
               
               
                 Br1 
                 Unknown 
                 23,906 
                   
                   
               
               
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 Total 
                 532,866 
                   
                 532,866 
               
               
                   
               
               
                   
                   
                 UA (from DP, 
                   
                   
               
               
                   
                   
                 deduplicated  
                 Gender 
                   
               
               
                   
                   
                 across all  
                 UA 
                 Redistr 
               
               
                 Brand 
                 Demo 
                 segments) 
                 Distr 
                 UA 
               
               
                   
               
               
                 Br1 
                 D1 = M12+ 
                 352 
                 42.5% 
                 360 
               
               
                 Br1 
                 D2 = F12+ 
                 476 
                 57.5% 
                 488 
               
               
                 Br1 
                 Unknown 
                 20 
                   
                   
               
               
                   
                 Gender 12+ 
                   
                   
                   
               
               
                 Br1 
                 Total 
                 848 
                   
                 848 
               
               
                   
               
            
           
         
       
     
     The example demographic distributor  318  receives as input post-Decision-Tree (DT) episode-level data from the DP  116  of  FIGS.  1  and/or  2    (e.g., via the DP data manager  316  of  FIG.  3   ). As shown in Table 1, the episode-level demographic data includes episode-level impression counts, episode-level durations, and episode-level unique audience counts, which correspond to a brand b, a sub-brand s, an asset p, and an episode e. The DP  116  aggregates and de-duplicates the impression counts, the durations, and the unique audience counts of Table 1 for all time segments included in an identified episode. The impression counts, the durations, and the unique audience counts of Table 1 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP  116 . The example DP  116  also provides the impression counts, the durations, and the unique audience counts of Table 1 for an unknown demographic group, which corresponds to impression counts, durations, and unique audience members that the DP  116  counted based on the pings or requests, but for which the DP  116  was unable to identify demographic groups. 
     Similarly, the example demographic distributor  318  receives as input post-Decision-Tree (DT) asset-level demographic data, sub-brand-level demographic data, and brand-level demographic data from the DP  116  of  FIGS.  1  and/or  2    (e.g., via the DP data manager  316  of  FIG.  3   ). As shown in Table 2, the asset-level demographic data includes asset-level impression counts, asset-level durations, and asset-level unique audience counts, which correspond to a brand b, a sub-brand s, and an asset p. The example impression counts, durations, and unique audience counts of Table 2 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP  116 . As shown in Table 3, the sub-brand level demographic data includes sub-brand level impression counts, sub-brand level durations, and sub-brand level unique audience counts, which correspond to a brand b, and a sub-brand s. The example impression counts, durations, and unique audience counts of Table 3 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP  116 . As shown in Table 4, the brand-level demographic data includes brand-level impression counts, brand-level durations, and brand-level unique audience counts, which correspond to a brand b. The example impression counts, durations, and unique audience counts of Table 4 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP  116 . The example demographic data of Tables 1-4 above may be further specified according to a platform and/or a device type, but the specified device type and the specified platform are omitted from Tables 1-4 for brevity. 
     Because the demographic distributor  318  may execute similar instructions to perform distribution at any of the episode level, the asset level, the sub-brand level, and the brand-level, the example instructions  500  of  FIG.  5    may be applied at the example episode level, the example asset level, the example sub-brand level, and/or the example brand-level. 
     In the example of  FIG.  5   , the example demographic distributor  318  of  FIG.  3    selects a combination of a brand b/sub-brand s/asset p/episode e, a platform y, a device type x, an age group a, and a gender group g (Block  502 ). In this example, the gender groups “male” and “female” are referred to by indices g=1 and 2, and an unknown gender group is referred to using the index g=0. 
     The demographic distributor  318  creates a gender impression distribution (e.g., gImpsRatio b/s/p/e,y,x,a,g ) (block  504 ). For example, the gender impression distribution gImpsRatio b/s/p/e,y,x,a,g  may be a ratio of the impression counts of Tables 1, 2, 3, and/or 4 for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and gender group g (e.g., Imps b/s/p/e,y,x,a,g ) and total impression counts across all of the gender groups (e.g., for g=1 and g=2) for the selected age group a and the selected brand b/sub-brand s/asset p/episode e. In the example of Table 1, the demographic distributor  318  determines an impression distribution of 59.6% for the male demographic group D 1  for the selected episode (e.g., 12,557/(12,557+8,496)) and 40.4% for the female demographic group D 2  for the selected episode (e.g., 8,496/(12,557+8,496). In the example of Table 2, the demographic distributor  318  determines an impression distribution of 56.8% for the male demographic group D 1  for the selected asset (e.g., 15,751/(15,751+11,987)) and 43.2% for the female demographic group D 2  for the selected asset (e.g., 11,987/(15,751+11,987)). In the example of Table 3, the demographic distributor  318  determines an impression distribution of 60% for the male demographic group D 1  for the selected sub-brand (e.g., 21,345/(21,345+16,789)) and 40.0% for the female demographic group D 2  for the selected sub-brand (e.g., 16,789/(21,345+16,789)). In the example of Table 4, the demographic distributor  318  determines an impression distribution of 66% for the male demographic group D 1  for the selected brand (e.g., 42,323/(42,323+21,789)) and 34% for the female demographic group D 2  for the selected brand (e.g., 21,789/(42,323+21,789)). Equation 1 below may be used to implement block  504 . 
     
       
         
           
             
               
                 
                   
                     
                       gImpsRatio 
                       
                         
                           
                             
                               b 
                               / 
                               s 
                             
                             / 
                             p 
                           
                           / 
                           e 
                         
                         , 
                         y 
                         , 
                         x 
                         , 
                         a 
                         , 
                         g 
                       
                     
                     = 
                     
                       
                         Imps 
                         
                           
                             
                               
                                 b 
                                 / 
                                 s 
                               
                               / 
                               p 
                             
                             / 
                             e 
                           
                           , 
                           y 
                           , 
                           x 
                           , 
                           a 
                           , 
                           g 
                         
                       
                       
                         
                           ∑ 
                           
                             g 
                             = 
                             1 
                           
                           g 
                         
                         ⁢ 
                         
                           ( 
                           
                             Imps 
                             
                               
                                 
                                   
                                     b 
                                     / 
                                     s 
                                   
                                   / 
                                   p 
                                 
                                 / 
                                 e 
                               
                               , 
                               y 
                               , 
                               x 
                               , 
                               a 
                               , 
                               g 
                             
                           
                           ) 
                         
                       
                     
                   
                   , 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       where 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       g 
                     
                     = 
                     
                       
                         1 
                         &amp; 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   ) 
                 
               
             
           
         
       
     
     The demographic distributor  318  creates a gender duration distribution (e.g., gDurationRatio b/s/p/e,y,x,a,g ) (Block  506 ). For example, the gender duration distribution gDurationRatio b/s/p/e,y,x,a,g  may be a ratio of the durations of Tables 1, 2, 3, and/or 4 for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and gender group g (e.g., Duration b/s/p/e,y,x,a,g ) and total durations across all of the gender groups (e.g., for g=1 and g=2) for the selected age group a and the selected brand b/sub-brand s/asset p/episode e. In the example of Table 1, the demographic distributor  318  determines a duration distribution of 60.4% for the male demographic group D 1  for the selected episode (e.g., 138,868 minutes/(138,868 minutes+91,004 minutes)) and 39.6% for the female demographic group D 2  for the selected episode (e.g., 91,004 minutes/(138,868 minutes+91,004 minutes). In the example of Table 2, the demographic distributor  318  determines a duration distribution of 54.7% for the male demographic group D 1  for the selected asset (e.g., 175,312 minutes/(175,312 minutes+145,119 minutes)) and 45.3% for the female demographic group D 2  for the selected asset (e.g., 145,119 minutes/(175,312 minutes+145,119 minutes)). In the example of Table 3, the demographic distributor  318  determines a duration distribution of 51.7% for the male demographic group D 1  for the selected sub-brand (e.g., 211,467 minutes/(211,467 minutes+197,651 minutes)) and 48.3% for the female demographic group D 2  for the selected sub-brand (e.g., 197,651 minutes/(211,467 minutes+197,651 minutes)). In the example of Table 4, the demographic distributor  318  determines a duration distribution of 57.4% for the male demographic group D 1  for the selected brand (e.g., 292,178 minutes/(292,178 minutes+216,782 minutes)) and 42.6% for the female demographic group D 2  for the selected brand (e.g., 216,782 minutes/(292,178 minutes+216,782 minutes)). Equation 2 below may be used to implement block  506 . 
     
       
         
           
             
               
                 
                   
                     
                       gDurationRatio 
                       
                         
                           
                             
                               b 
                               / 
                               s 
                             
                             / 
                             p 
                           
                           / 
                           e 
                         
                         , 
                         y 
                         , 
                         x 
                         , 
                         a 
                         , 
                         g 
                       
                     
                     = 
                     
                       
                         Duration 
                         
                           
                             
                               
                                 b 
                                 / 
                                 s 
                               
                               / 
                               p 
                             
                             / 
                             e 
                           
                           , 
                           y 
                           , 
                           x 
                           , 
                           a 
                           , 
                           g 
                         
                       
                       
                         
                           ∑ 
                           
                             g 
                             = 
                             1 
                           
                           g 
                         
                         ⁢ 
                         
                           ( 
                           
                             Duration 
                             
                               
                                 
                                   
                                     b 
                                     / 
                                     s 
                                   
                                   / 
                                   p 
                                 
                                 / 
                                 e 
                               
                               , 
                               y 
                               , 
                               x 
                               , 
                               a 
                               , 
                               g 
                             
                           
                           ) 
                         
                       
                     
                   
                   , 
                   
                     
 
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     
                       where 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       g 
                     
                     = 
                     
                       
                         1 
                         &amp; 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   ) 
                 
               
             
           
         
       
     
     The demographic distributor  318  creates a gender audience distribution (e.g., gUARatio b/s/p/e,y/x,a,g ) (Block  508 ). For example, the gender audience distribution gUARatio b/s/p/e,y/x,a,g  may be a ratio of the unique audience count of Tables 1, 2, 3, and/or 4 for the selected brand b/sub-brand s/asset p/episode e, platform y/device type x, age group a, and gender group g (e.g., UA b/s/p/e,y/x,a,g ) and total unique audience count across all of the gender groups (e.g., for g=1 and g=2) for the selected age group a and the selected brand b/sub-brand s/asset p/episode e. In some examples, the demographic distributor  318  creates the gender audience distribution gUARatio b,s,p,e,y,a,g  for a platform y but does not distinguish between device types x. In the example of Table 1, the demographic distributor  318  determines an audience distribution of 54.7% for the male demographic group D 1  for the selected episode (e.g., 105/(105+87)) and 45.3% for the female demographic group D 2  for the selected episode (e.g., 87/(105+87))). In the example of Table 2, the demographic distributor  318  determines an audience distribution of 38.5% for the male demographic group D 1  for the selected asset (e.g., 125/(125+200)) and 61.5% for the female demographic group D 2  for the selected asset (e.g., 200/(125+200))). In the example of Table 3, the demographic distributor  318  determines an audience distribution of 45.5% for the male demographic group D 1  for the selected sub-brand (e.g., 250/(250+300)) and 54.5% for the female demographic group D 2  for the selected sub-brand (e.g., 250/(250+300)). In the example of Table 4, the demographic distributor  318  determines an audience distribution of 42.5% for the male demographic group D 1  for the selected brand (e.g., 352/(352+476)) and 57.5% for the female demographic group D 2  for the selected brand (e.g., 476/(352+476)). Equation 3 below may be used to implement block  506 . 
     
       
         
           
             
               
                 
                   
                     
                       gUARatio 
                       
                         
                           
                             b 
                             / 
                             s 
                           
                           / 
                           p 
                         
                         , 
                         e 
                         , 
                         
                           y 
                           / 
                           x 
                         
                         , 
                         a 
                         , 
                         g 
                       
                     
                     = 
                     
                       
                         UA 
                         
                           
                             
                               
                                 b 
                                 / 
                                 s 
                               
                               / 
                               p 
                             
                             / 
                             e 
                           
                           , 
                           y 
                           , 
                           x 
                           , 
                           a 
                           , 
                           g 
                         
                       
                       
                         
                           ∑ 
                           
                             g 
                             = 
                             1 
                           
                           g 
                         
                         ⁢ 
                         
                           ( 
                           
                             UA 
                             
                               
                                 
                                   
                                     b 
                                     / 
                                     s 
                                   
                                   / 
                                   p 
                                 
                                 / 
                                 e 
                               
                               , 
                               y 
                               , 
                               x 
                               , 
                               a 
                               , 
                               g 
                             
                           
                           ) 
                         
                       
                     
                   
                   , 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       where 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       g 
                     
                     = 
                     
                       
                         1 
                         &amp; 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     3 
                   
                   ) 
                 
               
             
           
         
       
     
     The example demographic distributor  318  determines whether there are additional combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and known gender g (block  510 ). If there are additional combinations (e.g., combinations for which the distributions of the impression counts, the durations, and the unique audience counts are to be created) (block  510 ), control returns to block  502 . 
     When there are no more combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and known gender g (block  510 ), the demographic distributor  318  selects a combination of a brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a (block  512 ). 
     The demographic distributor  318  distributes the unknown gender impressions (block  514 ). For example, the demographic distributor  318  may distribute the unknown gender impressions to generate redistributed impressions rDPImps b/s/p/e,y,x,d  by applying the gender impression distribution gImpsRatio b/s/p/e,y,x,a,g  of Tables 1, 2, 3, and/or 4, to the impression counts, of Tables 1, 2, 3, and/or 4, respectively, for which the gender is unknown (e.g., unknown gender impressions) (Imps b/s/p/e,y,x,a,0 ) for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a. In the example of Table 1, the demographic distributor  318  determines 13,980 redistributed impression counts for the male demographic group D 1  (e.g., 12,557+0.596*2,387=13,980) and 9,460 redistributed impression counts for the female demographic group D 2  (e.g., 8,496+0.404*2,387=9,460) by applying (e.g., multiplying) the respective gender impression distribution of Table 1 to the unknown gender impressions. In the example of Table 2, the demographic distributor  318  determines 17,107 redistributed impression counts for the male demographic group D 1  (e.g., 15,751+0.568*2,387=17,107) and 13,018 redistributed impression counts for the female demographic group D 2  (e.g., 15,751+0432*2,387=13,108) by applying (e.g., multiplying) the respective gender impression distribution of Table 2 to the unknown gender impressions. In the example of Table 3, the demographic distributor  318  determines 22,777 redistributed impression counts for the male demographic group D 1  (e.g., 21,345+0.60*2,387=22,777) and 17,744 redistributed impression counts for the female demographic group D 2  (e.g., 16,789+0.40*2,387=17,744) by applying (e.g., multiplying) the respective gender impression distribution of Table 3 to the unknown gender impressions. In the example of Table 4, the demographic distributor  318  determines 43,898 redistributed impression counts for the male demographic group D 1  (e.g., 42,434+0.66*2,387=43,898) and 22,601 redistributed impression counts for the female demographic group D 2  (e.g., 21,789+0.34*2,387=22,601) by applying (e.g., multiplying) the respective gender impression distribution of Table 4 to the unknown gender impressions. As shown in Tables 1-4, the total DP impression counts are equal to the respective total redistributed impressions. Equation 4 below may be used to implement block  514 .
 
rDPImps b/s/p/e,y,x,d =Imps b/s/p/e,y,x,a,g +(Imps b/s/p/e,y,x,a,0 *gImpsRatio b/s/p/e,y,x,a,g )  (Equation 4)
 
     The demographic distributor  318  distributes the unknown gender durations (block  516 ). For example, the demographic distributor  318  may distribute the unknown duration impressions to generate redistributed durations rDPDuration b/s/p/e,y,x,d  by applying the gender duration distribution gDurationRatio b/s/p/e,y,x,a,g  of Tables 1, 2, 3, and/or 4, to the durations, of Tables 1, 2, 3, and/or 4, respectively, for which the gender is unknown (e.g., unknown gender durations) (Duration b/s/p/e,y,x,a,0 ) for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a. In the example of Table 1, the demographic distributor  318  determines a redistributed duration of 153,307 minutes for the male demographic group D 1  (e.g., 138,868 minutes+0.604*23,906 minutes=13,980 minutes) and a redistributed duration of 100,471 minutes for the female demographic group D 2  (e.g., 91,004 minutes+0.396*23,906 minutes=100,471 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 1 to the unknown gender duration. In the example of Table 2, the demographic distributor  318  determines a redistributed duration of 188,389 minutes for the male demographic group D 1  (e.g., 175,312 minutes+0.547*23,906 minutes=188,389 minutes) and a redistributed duration of 155,948 minutes for the female demographic group D 2  (e.g., 145,119 minutes+0.432*23,906 minutes=155,948 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 2 to the unknown gender durations. In the example of Table 3, the demographic distributor  318  determines a redistributed duration of 223,826 minutes for the male demographic group D 1  (e.g., 211,467 minutes+0.517*23,906 minutes=223,826 minutes) and a redistributed duration of 209,198 minutes for the female demographic group D 2  (e.g., 197,651 minutes+0.483*23,906 minutes=209,198 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 3 to the unknown gender duration. In the example of Table 4, the demographic distributor  318  determines a redistributed duration of 305,900 minutes for the male demographic group D 1  (e.g., 292,178 minutes+0.574*23,906 minutes=305,900 minutes) and a redistributed duration of 226,966 minutes for the female demographic group D 2  (e.g., 216,782 minutes+0.426*23,906 minutes=226,966 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 4 to the unknown gender duration. As shown in Tables 1-4, the total DP durations are equal to the respective total redistributed durations. Equation 5 below may be used to implement block  516 .
 
rDPDuration b/s/p/e,y,x,d =Duration b/s/p/e,y,x,a,g +(Duration b/s/p/e,y,x,a,0 *gDurationRatio b/s/p/e,y,x,a,g )  (Equation 5)
 
     The demographic distributor  318  distributes the unknown gender audience (block  518 ). For example, the demographic distributor  318  may distribute the unknown gender audience to generate redistributed unique audiences rDPUA b/s/p/e,y/x,d  by applying the gender audience distribution gUARatio b/s/p/e,y,x,a,g  of Tables 1, 2, 3, and/or 4, to the unique audience counts, of Tables 1, 2, 3, and/or 4, respectively, for which the gender is unknown (e.g., unknown gender audience) (UA b/s/p/e,y,x,a,0 ) for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a. In the example of Table 1, the demographic distributor  318  determines 116 redistributed unique audience members (e.g., a redistributed unique audience count) for the male demographic group D 1  (e.g., 105+0.547*20=116) and 96 redistributed unique audience members for the female demographic group D 2  (e.g., 87+0.453*20=96) by applying (e.g., multiplying) the respective gender audience distribution of Table 1 to the unknown gender unique audience. In the example of Table 2, the demographic distributor  318  determines 133 redistributed unique audience members for the male demographic group D 1  (e.g., 125+0.386*20=133) and 212 redistributed unique audience members for the female demographic group D 2  (e.g., 200+0.615*20=212) by applying (e.g., multiplying) the respective gender audience distribution of Table 2 to the unknown gender unique audience. In the example of Table 3, the demographic distributor  318  determines 259 redistributed unique audience members for the male demographic group D 1  (e.g., 250+0.455*20=259) and 311 redistributed unique audience members for the female demographic group D 2  (e.g., 300+0.545*20=311) by applying (e.g., multiplying) the respective gender audience distribution of Table 3 to the unknown gender unique audience. In the example of Table 4, the demographic distributor  318  determines 360 redistributed unique audience members for the male demographic group D 1  (e.g., 352+0.425*20=360) and 488 redistributed unique audience members for the female demographic group D 2  (e.g., 476+0.575*20=488) by applying (e.g., multiplying) the respective gender audience distribution of Table 4 to the unknown gender unique audience. As shown in Tables 1-4, the total DP unique audiences are equal to the respective total redistributed unique audiences. Equation 6 below may be used to implement block  518 .
 
rDPUA b/s/p/e,y/x,d   =UA   b/s/p/e,y/x,a,g +( UA   b/s/p/e,y/x,a,0 *gUARatio b/s/p/e,y/x,a,g )  (Equation 6)
 
     In examples disclosed herein, the redistributed audience (e.g., rDPUA b/s/p/e,y,x,a,g ), when determined at the episode level, is determine for each platform y but not device type x. 
     The demographic distributor  318  determines whether there are additional combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a (block  520 ). If there are additional combinations (e.g., combinations for which the redistributed impression counts, the redistributed durations, and the redistributed unique audience counts are to be created) (block  520 ), control returns to block  512 . When there are no more combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a (block  520 ), the example instructions  500  of  FIG.  5    end. 
     In some other examples, the instructions  500  of  FIG.  5    are performed for combinations of a first time segment level including demographic data corresponding to a brand b, sub-brand s, asset p, segment z; a second time segment level including demographic data corresponding to a brand b and a segment z; a cross-publisher level including demographic data corresponding to a platform y, a brand b, a segment z, and an age group a; and/or a text/video (T/V) segment level including demographic data corresponding to a content type c (e.g., either text content t, video content v, or both text content and video content w), a brand b, a segment z, and a platform y. 
       FIG.  6    is a flowchart representative of example machine readable instructions  600  which may be executed to implement the audience data generator  120  of  FIGS.  1 - 3    to calculate raw measures for the demographic groups using redistributed demographic information. The example instructions  600  are described below with reference to the example distributed demographic data calculator  320  of  FIG.  3   . 
     The example instructions  600  may be executed to implement the distributed demographic data calculator  320  to calculate frequencies and deduplication factors at the episode-level e, the asset-level p, the sub-brand level s, the brand-level b, the second time segment level, and/or the cross-publisher level. In some examples, the example instructions  600  may include calculating duration-per-impression (DPI) measures at the first time segment level. In examples disclosed herein, the distributed demographic data calculator  320  receives redistributed demographic data from the demographic distributor  318 . The redistributed demographic data received from the demographic distributor includes redistributed impressions (e.g., rDPImps b/s/p/e,y,x,d  (e.g., at the example episode-level e, the example asset-level p, the example sub-brand level s, the example brand-level b, the example first time segment level, the example second time segment level, and/or the example cross-publisher level), redistributed audiences rDPUA b/s/p/e,y/x,d  (e.g., at the example episode-level e, the example asset-level p, the example sub-brand level s, and the example brand-level b, and/or the example cross-publisher level), and/or redistributed durations rDPDuration b,s,p,i,y,x,d  (e.g., at the example first time segment level). 
     The example episode FC  343  receives the redistributed impressions rDPImps b,s,p,e,y,x,d  and the redistributed audiences rDPUA b,s,p,e,y,d  at the at the example episode-level e, the example asset-level p, the example sub-brand level s, the example brand-level b, the example first time segment level, the example second time segment level, and/or the example cross-publisher level (e.g., from  FIG.  5   ). Additionally, the episode DFG  344  receives the redistributed audiences rDPUA b,s,p,e,y,d  at the at the example episode-level e, the example asset-level p, the example sub-brand level s, the example brand-level b, the example first time segment level, the example second time segment level, and/or the example cross-publisher level. In the example instructions  600  of  FIG.  6   , the episode FC  343  calculates raw database proprietor frequency for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d (e.g., rDPFreq b,s,p,e,y,d ) (block  602 ). The episode FC  343  calculates the episode-level frequency rDPFreq b,s,p,e,y,d  by dividing, for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d, the redistributed impressions (e.g., rDPImps b,s,p,e,y,x,d ) across all device types x (e.g., rDPImps b,s,p,e,y,d ) by the redistributed audiences (e.g., rDPUA b,s,p,e,y,d ). In some examples, the episode-level frequency rDPFreq b,s,p,e,y,d  is not calculated for each device type x at the episode-level e because the unique audience counts at the episode-level e (e.g., UA b,s,p,e,y,a,g ) provided by the DP  116  of  FIGS.  1  and/or  2    are not organized by device type x. Equation 7 below may be used to implement block  602 .
 
rDBFreq b,s,p,e,y,d =rDPImps b,s,p,e,y,d /rDPUA b,s,p,e,y,d   (Equation 7)
 
     The episode DFG  344  calculates a deduplication factor (e.g., Dedup_F TD,b,s,p,e,d ) for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d (block  604 ). For example, the episode DFG  344  may calculate, for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d, the episode-level deduplication factor Dedup_F TD,b,s,p,e,d  by dividing an episode-level gender audience attributed to both the mobile computing platform and the non-mobile computing platform (e.g., the total digital platform), (e.g., rDPUA TD,b,s,p,e,d ), a duplicated episode-level audience, by a sum of an episode-level gender audience attributed to the non-mobile computing platform (e.g., rDPUA Desktop,b,s,p,e,d ) and an episode-level gender audience attributed to the mobile computing platform (e.g., rDPUA Mobile,b,s,p,e,d ). Equation 8 below may be used to implement block  604 .
 
Dedup_ F   TD,b,s,p,e,d =rDPUA TD,b,s,p,e,d /rDPUA Desktop,b,s,p,e,d +rDPUA Mobile,b,s,p,e,d   (Equation 8)
 
     After calculating the deduplication factor, the example instructions  600  end. In some examples, the raw DP frequency (block  602 ) and the deduplication factor (block  604 ) may be calculated for combinations of brand b, sub-brand s, asset p, platform y, device type x, and demographic group d; combinations of brand b, sub-brand s, platform y, device type x, and demographic d; and/or Calculate raw DP frequency for each brand b, platform y, device type x, and demographic group d. 
       FIG.  7    is a flowchart representative of example machine readable instructions  700  which may be executed to implement the audience data generator of  FIGS.  1 - 3    to calculate a frequency and a deduplication factor associated with database proprietor demographic data at the time segment level and the cross-publisher level. 
     The distributed demographic data calculator  320  calculates raw measures associated with demographic data redistributed at the first time segment level, the second time segment level, and the cross-publisher level based on the example instructions  700  of  FIG.  7   . In examples disclosed herein, the DPI calculator  340  receives the redistributed impressions rDPImps b,s,p,i,y,x,d  and the redistributed durations rDPDuration b,s,p,i,y,x,d  from the demographic distributor  318  (e.g., at the first time segment level). The segment FC  341  receives the redistributed impressions rDPImps b,i,y,x,d  and the redistributed audiences rDPUA b,i,y,x,d  from the demographic distributor  318  (e.g., at the second time segment level). The segment DFG  342  receives the redistributed audiences rDPUA b,i,y,x,d  from the demographic distributor  318  (e.g., at the second time segment level). Additionally, the CP-FC  351  receives the redistributed impressions rDBCPImps b,i,y,d  and the redistributed audience counts rDBCPUA b,i,y,d  from the demographic distributor  318  (e.g., cross-publisher level). 
     The DPI calculator  340  of  FIG.  3    calculates a duration-per-impression (DPI) measure (e.g., rDPDPI b,s,p,i,y,x,d ) (block  702 ). For example, the DPI calculator  340  may calculate the DPI measure, for each combination of brand b, sub-brand s, asset p, segment z, platform y, device type x, and demographic d, by dividing a respective redistributed duration rDPDuration b,s,p,i,y,x,d  by a respective redistributed impressions rDPImps b,s,p,i,y,x,d . Equation 9 below may be used to implement block  702 .
 
rDPDPI b,s,p,i,y,x,d =rDPDuration b,s,p,i,y,x,d /rDPImps b,s,p,i,y,x,d   (Equation 9)
 
     The segment FC  341  calculates a segment level frequency (e.g., rDPFreq b,i,y,d ) (block  704 ). For example, the segment FC  341  may calculate the segment level frequency rDPFreq b,i,y,d  (e.g., at the second time segment level) by dividing, for each combination of brand b, segment z, platform y, and demographic d, respective redistributed impressions rDPImps b,i,y,x,d  across all device types x (e.g., rDPImps b,i,y,d ) by a respective redistributed audience rDPUA b,i,y,x,d  across all device types x (e.g., rDPUA b,i,y,d ). Equation 10 below may be used to implement block  704 .
 
rDPFreq b,i,y,d =rDPImps b,i,y,d /rDPUA b,i,y,d   (Equation 10)
 
     The segment DFG  342  calculates a segment level deduplication factor (e.g., Dedup_F TD,b,i,d ) (block  706 ). For example, the segment DFG  342  may calculate, for each combination of brand b, segment z, and demographic group d, the segment-level deduplication factor Dedup_F TD,b,i,d  by dividing a segment level gender audience attributed to the total digital platform (e.g., rDPUA TD,b,i,d ), a duplicated segment-level audience, by a sum of a segment-level gender audience attributed to the non-mobile computing platform (e.g., rDPUA Desktop,b,i,d ) and a segment-level gender audience attributed to the mobile computing platform (e.g., rDPUA Mobile,b,i,d ). In some examples, the duplicated segment-level audience (e.g., rDPUA TD,b,i,d ), the segment-level gender audience attributed to the non-mobile computing platform (e.g., rDPUA Desktop,b,i,d ), and the segment-level gender audience attributed to the mobile computing platform (e.g., rDPUA Mobile,b,i,d ) are derived from the redistributed audiences rDPUA b,i,y,x,d  across all device types x. Equation 11 below may be used to implement block  706 .
 
Dedup_ F   TD,b,i,d =rDPUA TD,b,i,d /(rDPUA Desktop,b,i,d +rDPUA Mobile,b,i,d )  (Equation 11)
 
     The CP-FC  351  calculates a cross-publisher level frequency (e.g., rDPCPFreq b,i,y,d ) (block  708 ). For example, the CP-FC  351  may calculate the cross publisher level frequency rDPCPFreq b,i,y,d  by dividing, for each combination of brand b, segment z, platform y, and demographic d, respective redistributed impressions rDPCPImps b,i,y,x,d  across all device types x (e.g., rDPCPImps b,i,y,d ) by a respective redistributed audience rDPCPUA b,i,y,x,d  across all device types x (e.g., rDPCPUA b,i,y,d ). Equation 12 below may be used to implement block  708 .
 
rDPCPFreq b,i,y,d =rDPCPImps b,i,y,d /rDPCPUA b,i,y,d   (Equation 12)
 
     The CP-DFG  352  calculates a cross publisher level deduplication factor (e.g., Dedup_F_CP TD,b,i,d ) (block  710 ). For example, the CP-DFG  352  may calculate, for each combination of brand b, segment z, and demographic group d, the cross-publisher level deduplication factor Dedup_F_CP TD,b,i,d  by dividing a cross-publisher level gender audience attributed to the total digital platform (e.g., rDPCPUA TD,b,i,d ), a duplicated cross publisher level audience, by a sum of a cross-publisher level gender audience attributed to the non-mobile computing platform (e.g., rDPCPUA Desktop,b,i,d ) and a cross-publisher level gender audience attributed to the mobile computing platform (e.g., rDPCPUA Mobile,b,i,d ). Equation 13 below may be used to implement block  710 .
 
Dedup_ F _ CP   TD,b,i,d =rDPCPUA TD,b,i,d /(rDPCPUA Desktop,b,i,d +rDPUA Mobile,b,i,d )  (Equation 13)
 
     The example instructions  700  end. 
     Misattribution Correction Matrix Generation and Transformation 
       FIG.  8    illustrates an example misattribution correction matrix  800  (e.g., for a particular brand b and device type x) generated by the matrix generator  322 . The example matrix generator  322  may generate the example misattribution correction matrix  800  as described in U.S. Patent Publication No. 2015/0262207. The entirety of U.S. Patent Publication No. 2015/0262207 is incorporated herein by reference. In the illustrated example of  FIG.  8   , the columns of the misattribution correction matrix  800  (database proprietor demographic groups i  802 ) represent demographic groups identified by the DP  116  associated with impression information, and the rows of the misattribution correction matrix  800  (actual demographic groups j  804 ) represent the demographic groups actually viewing the media  206  of  FIG.  2    and corresponding to the survey calibration data. 
     Each cell of the misattribution correction matrix  800  corresponding to a demographic group i  802  and a demographic group j  804  includes a probability (e.g., represented as a percentage). In examples disclosed herein, the probability is a probability that (1) a person of the demographic group j is identified by the DP  116  as a viewer of the media  206  and (2) a person in the actual demographic group j is an actual viewer of the media  206 . Thus, when a person is identified by the DP  116  as being a person in the demographic group i, the actual or true viewer is a person in the actual viewer demographic group j, determined from the survey calibration data. As shown in  FIG.  8   , the values of each of the columns sum to 100%. Therefore, all of the impression information attributed to recognized demographic group i is accounted for. 
       FIG.  9    is a flow chart representative of example machine readable instructions  900  which may be executed to implement the audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  to convert or transform the misattribution correction matrices into impression count matrices IM b,s,p,y,x,d . The example matrix converter  353  of  FIG.  3    may perform the instructions  900  of  FIG.  9    to convert the misattribution correction matrices generated by the matrix generator  322  to impression count matrices for each combination of brand b and device type x. In examples disclosed herein, the impression count matrices are generated using the asset-level frequencies rDPFreq b,s,p,y,x,d  generated by the asset FC  345  of  FIG.  3   . The matrix converter  353  may apply a conversion formula, or a conversion equation, to each cell of each of the misattribution correction matrices to convert the misattribution correction matrices to impression count matrices. 
     The matrix converter  353  may obtain asset-level frequencies (e.g., rDPFreq b,s,p,y,x,d ) from the asset FC  345  of  FIG.  3   . The matrix converter  353  also obtains and/or accesses the misattribution correction matrices (e.g., the example misattribution correction matrix  800  of  FIG.  8   ) generated by the matrix generator  322  for each combination of brand b and device type x. 
     The matrix converter  353  selects a misattribution correction matrix  800  for a combination of a device type x, a platform y, a brand b, a sub-brand s and an asset p (block  902 ). 
     The matrix converter  353  selects a combination of demographic groups i and j in the selected misattribution matrix  800  (block  904 ). For example, the matrix converter  353  may select one of the demographic groups i and one of the demographic groups j of the selected misattribution matrix. To illustrate, the matrix converter  353  may select the “D 1 ” (e.g., Female, ages 02-12) demographic group from the database proprietor demographic group i  802  and the “D 1 ” (e.g., Female, ages 02-12) demographic group from the actual demographic group j  804  in the misattribution correction matrix  800 . 
     The matrix converter  353  converts a misattribution probability of the selected combination of demographic groups i and j to an impression probability (block  906 ). For example, the matrix converter  353  may use Equation 14 below to implement block  906  for a selected probability (e.g., cell) of the misattribution matrix  800  of  FIG.  9    to convert each cell of a misattribution matrix to an impression probability.
 
 IM   b,s,p,y,x,DPDemo     p     TrueDemo     j   =( M   b,x,DPDemo     p     TrueDemo     j×rDPFreq     b,s,p,y,x,d )/(Σ j=1   d   M   b,x,DPDemo     p     TrueDemo     j×rDPFreq     b,s,p,y,x,d )  (Equation 14)
 
     The matrix converter  353  determines whether there are additional combinations of demographic groups i and j (block  908 ). If there are additional combinations of demographic groups i and j, control returns to block  904  to select another combination of demographic groups i and j. When there are no more combinations of the demographic groups i and j (block  908 ), the example matrix converter  353  determines whether there are additional combinations of platform y, device type x, brand b, sub-brand s, and asset p (block  910 ). If there are additional combinations, the example instructions  900  return to block  902  to select another misattribution correction matrix for conversion. In this manner, the matrix converter  353  converts each misattribution correction matrix corresponding to a brand b and a device type x to an impression counts matrix including impression probabilities for each brand b, sub-brand s, asset p, platform y, and device type x. When there are no additional combinations of platform y, device type x, brand b, sub-brand s, and asset p (block  910 ), the example instructions  900  of  FIG.  9    end. 
     Sharing Adjustments, Coverage Adjustments, and Scaling at the Episode-Level and the First Time Segment Level 
       FIG.  10    is a flow chart representative of example machine readable instructions  1000  which may be executed to implement the audience data generator  120  of  FIGS.  1 - 3    to compensate impression data for inaccuracies related to demographic misattribution and database proprietor coverage at the episode level e and the first time segment level. The example E-S adjuster  324  of  FIG.  3    may perform the instructions  1000  of  FIG.  10    to adjust the redistributed impressions rDPImps b,s,p,e,y,x,d  and rDPImps b,s,p,i,y,x,d  at the episode-level and the first time segment level, respectively, and to adjust the redistributed durations rDPDuration b,s,p,e,y,x,d  and rDPDuration b,s,p,i,y,x,d  at the episode-level and the first time segment level, respectively, to compensate for misattribution and coverage error. 
     The impression sharing adjuster  354  and the duration sharing adjuster  355  receive the redistributed impressions rDPImps b,s,p,e,y,x,d  and rDPImps b,s,p,i,y,x,d  and the redistributed durations rDPDuration b,s,p,e,y,x,d  and rDPDuration b,s,p,i,y,x,d  from the demographic distributor  318  of  FIG.  3   . Additionally, the impression sharing adjuster  354  receives and/or obtains the impression count matrices from the matrix converter  353  of  FIG.  3   . 
     Because the E-S adjuster  324  may execute similar instructions to perform the example instructions  1000  at any of the episode-level and the first time segment level, the example instructions  1000  of  FIG.  10    are described below using the episode level and the first time segment level in the alternative (e.g., episode/segment). 
     The E-S adjuster  324  selects a combination of platform y, device type x, brand b, sub-brand s, episode e/segment z, and demographic d (block  1002 ). The E-S adjuster  324  selects an impression counts matrix (block  1004 ). For example, the E-S adjuster  324  selects an impression matrix corresponding to the selected platform y, device type x, brand b, sub-brand s, and asset p. The impressions sharing adjuster  354  determines misattribution adjusted impressions (block  1006 ). In examples disclosed herein, the impressions sharing adjuster  354  applies (e.g., performs a dot product calculation) the selected impression counts matrix (e.g., IM b,s,p,y,x,d ) to the redistributed impressions (e.g., rDPImps b,s,p,e,y,x,d /rDPImps b,s,p,i,y,x,d ) corresponding to the selected combination of platform y, device type x, brand b, sub-brand s, episode e/segment z, and demographic d. For example, the impressions sharing adjuster  354  multiplies each cell of the impression counts matrix to the redistributed impressions to determine misattribution adjusted impressions (e.g., mImps b,s,p,y,x,e/i,d ) for each demographic d. 
     The duration sharing adjuster  355  determines misattribution adjusted durations (block  1008 ). In examples disclosed herein, the duration sharing adjuster  355  applies (e.g., performs a dot product calculation) the selected impression counts matrix (e.g., IM b,s,p,y,x,d ) to the redistributed durations (e.g., rDPDuration b,s,p,e,y,x,d /rDPDuration b,s,p,i,y,x,d ) corresponding to the selected combination of platform y, device type x, brand b, sub-brand s, episode e/segment z, and demographic d. For example, the duration sharing adjuster  355  multiplies each cell of the impression counts matrix to the redistributed durations to determine misattribution adjusted durations (e.g., mDuration b,s,p,y,x,e/i,d ) for each demographic d. 
     The coverage vector calculator  356  calculates coverage adjustment vectors (block  1010 ). The coverage vector calculator  356  receives survey calibration data from the calibration data collector  300  of  FIG.  3   . The coverage vector calculator  356  calculates coverage adjustment vectors (e.g., C b,x ) for each combination of brand b and device type x using the survey calibration data. The coverage vector calculator determines a total number of respondents in a demographic group with access to a device of a device type x and media (e.g., the media  206  of  FIG.  2   ) provided by a brand b and divides by a covered portion of respondents (e.g., recognized by the DP  116 ) in the demographic group for a device type x and a brand b. 
     The impressions coverage adjuster  357  calculates coverage adjusted impressions (block  1012 ). In examples disclosed herein, the impressions coverage adjuster  357  receives the misattribution adjusted impressions mImps b,s,p,y,x,e/i,d  from the impressions sharing adjuster  354  and the coverage adjustment vectors C b,x  from the coverage vector calculator  356 . The impressions coverage adjuster  357  multiplies the misattribution adjusted impressions by a coverage adjustment vector C b,x  corresponding to the selected brand b and device type x. The resultant product is coverage adjusted impressions cImps b,s,p,e/i,y,x,d . Equation 15 below may be used to implement block  1012 .
 
cImps b,s,p,e/i,y,x,d =mImps b,s,p,e/i,y,x,d   *C   b,x   (Equation 15)
 
     The duration coverage adjuster  358  calculates coverage adjusted durations (block  1014 ). In examples disclosed herein, the duration coverage adjuster  358  receives the misattribution adjusted durations mDuration b,s,p,y,x,e/i,d  from the duration sharing adjuster  355  and the coverage adjustment vectors C b,x  from the coverage vector calculator  356 . The duration coverage adjuster  358  multiplies misattribution adjusted durations by a coverage adjustment vector C b,x  corresponding to the selected brand b, and device type x. The resultant product is coverage adjusted durations cDuration b,s,p,e/i,y,x,d . Equation 16 below may be used to implement block  1014 .
 
cDuration b,s,p,e/i,y,x,d =mDuration b,s,p,e/i,y,x,d   *C   b,x   (Equation 16)
 
     The E-S adjuster  324  determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block  1016 ). If there are additional combinations, the example instructions  1000  return to block  1002 . When the E-S adjuster  324  determines no additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d are available (block  1016 ), the E-S adjuster  324  obtains census impression counts SCImps b,s,p,e/i,y,x,d  and census durations SCDuration b,s,p,e/i,y,x,d  from the monitored website (e.g., the web site census) corresponding to the brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block  1018 ). 
     The E-S adjuster  324  selects a combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block  1020 ). The impression scaling factor generator  359  obtains the census impression counts SCImps b,s,p,e/i,y,x,d  and receives the coverage adjusted impressions cImps b,s,p,e/i,y,x,d  from the impressions coverage adjuster  357 . The impression scaling factor generator  359  determines an impression scaling factor (block  1022 ). The impression scaling factor generator  359  generates, or calculates, for the selected combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, an impression scaling factor ISF b,s,p,e/i,y,x,d  by dividing a corresponding census impression count SCImps b,s,p,e/i,y,x,d  by a sum of corresponding coverage adjusted impressions across all demographic groups d. Equation 17 below may be used to implement block  1022 . 
     
       
         
           
             
               
                 
                   
                     
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     The duration scaling factor generator  360  obtains the census durations SCDuration b,s,p,e/i,y,x,d  and receives the coverage adjusted durations cDuration b,s,p,e/i,y,x,d  from the duration coverage adjuster  358 . The duration scaling factor generator  360  determines a duration scaling factor (block  1024 ). The duration scaling factor generator  360  calculates, for the selected combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, a duration scaling factor DSF b,s,p,e/i,y,x,d  by dividing a corresponding census duration SCDuration b,s,p,e/i,y,x,d  by a sum of corresponding coverage adjusted durations across all demographic groups d. Equation 18 below may be used to implement block  1024 . 
     
       
         
           
             
               
                 
                   
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                           d 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     18 
                   
                   ) 
                 
               
             
           
         
       
     
     The impression scaler  361  applies the impression scaling factor to the coverage adjusted impressions to determine scaled impressions (block  1026 ). In examples disclosed herein, the impressions scaler  361  receives the coverage adjusted impressions cImps b,s,p,e/i,y,x,d  from the impressions coverage adjuster  357  and the impression scaling factor ISF b,s,p,e/i,y,x,d  from the impression scaling factor generator  359 . For each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the impression scaler  361  applies (e.g., multiplies), corresponding coverage adjusted impressions by the impression scaling factor to determine scaled impressions sImp b,s,p,e/i,y,x,d . Equation 19 below may be used to implement block  1026 .
 
sImps b,s,p,e/i,y,x,d =cImps b,s,p,e/i,y,x,d   *ISF   b,s,p,e/i,y,x,d   (Equation 19)
 
     The duration scaler  362  applies the duration scaling factor to the coverage adjusted durations to determine scaled durations (block  1028 ). In examples disclosed herein, the duration scaler  362  receives the coverage adjusted durations cDuration b,s,p,e/i,y,x,d  from the duration coverage adjuster  358  and the duration scaling factor DSF b,s,p,e/i,y,x,d  from the duration scaling factor generator  360 . For each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the duration scaler  362  applies (e.g., multiplies), corresponding coverage adjusted durations by the duration scaling factor to determine scaled durations sDuration b,s,p,e/i,y,x,d . Equation 20 below may be used to implement block  1028 .
 
 s Duration b,s,p,e/i,y,x,d   =c Duration b,s,p,e/i,y,x,d *DSF b,s,pe/i,y,x,d   (Equation 20)
 
     The E-S adjuster  324  determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block  1030 ). If there are additional combinations, the example instructions return to block  1020 . When the E-S adjuster  324  determines there are no additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block  1030 ), the example instructions  1000  of  FIG.  10    end. 
     Adjusting, Scaling, and Imputation at the Episode Level and the First Time Segment Level 
       FIG.  11    is a flow chart representative of example machine readable instructions  1100  which may be executed to implement the audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  to adjust database proprietor demographic data and to impute cite census impression information. The example census imputer  326  of  FIG.  3    may perform the instructions  1100  of  FIG.  11    to adjust the scaled impressions sImp b,s,p,e/i,y,x,d  and the scaled durations sDuration b,s,p,e/i,y,x,d  at the asset-level and to impute cite census impressions SCImps b,s,p,e/i,y,x,d  and cite census durations SCDuration b,s,p,e/i,y,x,d . 
     Because the census imputer  326  may execute similar instructions to perform the example instructions  1100  at any of the episode-level and the first time segment level, the example instructions  1100  of  FIG.  11    are described below using the episode level and the first time segment level in the alternative (e.g., episode/segment). 
     The census imputer  326  obtains the scaled impressions sImp b,s,p,e/i,y,x,d  and the scaled durations sDuration b,s,p,e/i,y,x,d  from the E-S adjuster  324  (block  1102 ). In examples disclosed herein, the impressions adder  363  obtains the scaled impressions sImp b,s,p,e/i,y,x,d  and the duration adder  364  obtains the scaled durations sDuration b,s,p,e/i,y,x,d . The census imputer  326  selects a combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d (block  1104 ). 
     The impressions adder  363  calculates asset-level adjusted impressions (block  1106 ). In examples disclosed herein, the impressions adder  363  adds, or sums, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, corresponding scaled impressions sImp b,s,p,e/i,y,x,d  across all episodes e or segments i included in the selected asset p. For example, the impressions adder  363  scales, or rolls up, the scaled impressions across all episodes e/segments i of the selected asset p to generate asset-level adjusted impressions as e/i Imps b,s,p,y,x,d . Equation 21 below may be used to implement block  1106 . In Equation 21, n is the total number of episode/segment components.
 
 as   e/i Imps b,s,p,y,x,d =Σ e/i=1   n sImps b,s,p,e/i,y,x,d   (Equation 21)
 
     The duration adder  364  calculates asset-level adjusted durations (block  1108 ). In examples disclosed herein, the duration adder  364  adds, or sums, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, corresponding scaled durations sDurations b,s,p,e/i,y,x,d  across all episodes e/segments i included in the selected asset p. For example, the duration adder  364  scales, or rolls up, the scaled durations across all episodes e/segments i of the selected asset p to generate asset-level adjusted durations as e/i Duration b,s,p,y,x,d . Equation 22 below may be used to implement block  1108 . In Equation 22, n is the total number of episode/segment components.
 
 as   e/i Duration b,s,p,y,x,d =Σ e/i=1   n sDuration b,s,p,e/i,y,x,d   (Equation 22)
 
     The census imputer  326  determines whether there are any additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block  1110 ). If there are additional combinations (block  1110 ), the example instructions  1100  return to block  1104 . 
     When the census imputer  326  determines there are no additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block  1110 ), the census imputer  326  selects a demographic d (block  1112 ). The impressions distribution deriver  367  receives the asset-level adjusted impressions as e/i Imps b,s,p,y,x,d  and the sum of the asset-level adjusted impressions across all demographic groups d (Σ d=1   n  as e/i Imps b,s,p,y,x,d ) from the impressions adder  363  and the impressions imputation adder  365 , respectively. 
     The impressions distribution deriver  367  distributes the asset-level adjusted impressions (block  1114 ). In examples disclosed herein, the impressions distribution deriver  367  distributes the asset-level adjusted impressions as e/i Imps b,s,p,y,x,d  by dividing, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, a corresponding asset-level adjusted impression by a corresponding sum of asset-level adjusted impressions across all demographics d. The resulting distribution is referred to herein as an adjusted and scaled impression demographic distribution as e/i ImpDemoDistr b,s,p,y,x,d . Equation 23 below may be used to implement block  1114 .
 
 as   e/i ImpDemoDistr b,s,p,y,x,d   =as   e/i Imps b,s,p,y,x,d /(Σ d=1   n   as   e/i Imps b,s,p,y,x,d )  (Equation 23)
 
     The duration distribution deriver  368  receives the asset-level adjusted durations as e/i Duration b,s,p,y,x,d  and the sum of the asset-level adjusted durations across all demographic groups d (Σ d=1   n  as e/i Duration b,s,p,y,x,d ) from the duration adder  364  and the duration imputation adder  365 , respectively. The duration distribution deriver  368  distributes the asset-level adjusted durations (block  1116 ). In examples disclosed herein, the duration distribution deriver  368  distributes the asset-level adjusted durations as e/i Duration b,s,p,y,x,d  by dividing, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, a corresponding asset-level adjusted duration by a corresponding sum of asset-level adjusted durations across all demographics d. The resulting distribution is referred to herein as an adjusted and scaled duration demographic distribution as e/i DurationDemoDistr b,s,p,y,x,d . Equation 24 below may be used to implement block  1116 .
 
 as   e/i DurationDemoDistr b,s,p,y,x,d   =as   e/i Durations b,s,p,y,x,d /(Σ d=1   n   as   e/i Duration b,s,p,y,x,d )  (Equation 24)
 
     The census imputer  326  selects an episode e/segment z (block  1118 ). The impressions imputation director  369  subsequently accesses the site census impressions SCImps b,s,p,e/i,y,x,d . The impressions imputation director  369  also receives and/or accesses the adjusted and scaled asset-level impression demographic distribution as e/i ImpDemoDistr b,s,p,y,x,d  from the impressions distribution deriver  367 . The impressions imputation director  369  determines whether there are site census impressions logged and no DP impressions logged for the selected episode e/segment z (block  1120 ). For example, there may be some site census impressions SCImps b,s,p,e/i,y,x,d  identified by the impressions imputation director  369  for which DP impressions have not been received (e.g., the DP  116  of  FIGS.  1  and/or  2    did not recognize any impressions for the episode e/segment z selected at block  1118 ), but one or more impressions have been received from the site census. When there are site census impressions SCImps b,s,p,e/i,y,x,d  logged and no DP impressions for the selected episode e/segment z (block  1120 ), the impressions imputation director  369  imputes the census impressions with the adjusted and scaled asset-level impression distribution (block  1122 ). For example, the impressions imputation director  369  may apply (e.g., multiply), for each combination of selected brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the adjusted and scaled impression demographic distribution as e/i ImpDemoDistr b,s,p,y,x,d  to the site census impressions SCImps b,s,p,e/i,y,x,d  to generate imputed impressions i e/i Imp p,b,s,p,y,x,d . Equation 25 below may be used to implement block  1122 .
 
 i   e/i   Imp   p,b,s,p,y,x,d   =as   e/i ImpDemoDistr b,s,p,y,x,d *SCImps b,s,p,e/i,y,x,d   (Equation 25)
 
     After imputing the impressions (block  1122 ), or when the impressions distribution director  369  determines there are no site census impressions logged and no DP impressions for the selected episode e/segment z (block  1120 ), the example duration imputation director  370  determines whether there are site census durations logged and no DP durations logged for the selected episode e/segment z (block  1124 ). For example, there may be some site census durations SCDuration b,s,p,e/i,y,x,d  identified by the duration imputation director  370  for which DP durations have not been received (e.g., the DP  116  of  FIGS.  1  and/or  2    did not recognize any durations for the episode e/segment z selected at block  1118 ), but one or more durations have been received from the site census. The duration imputation director  370  accesses the site census durations SCDuration b,s,p,e/i,y,x,d . The duration imputation director  370  also receives and/or accesses the adjusted and scaled asset-level duration demographic distribution as e/i DurationDemoDistr b,s,p,y,x,d  from the duration distribution deriver  368 . When there are site census durations SCDurations b,s,p,e/i,y,x,d  logged and no DP durations for the selected episode e/segment z, (block  1124 ), the duration imputation director  370  imputes the census durations with the adjusted and scaled asset-level duration distribution (block  1126 ). For example, the duration imputation director  370  may apply (e.g., multiply), for each combination of selected brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the adjusted and scaled duration demographic distribution as e/i DurationDemoDistr b,s,p,y,x,d  to the site census durations SCDuration b,s,p,e/i,y,x,d  to generate imputed durations i e/i Duration p,b,s,p,y,x,d . Equation 26 below may be used to implement block  1126 .
 
 i   e/i Duration p,b,s,p,y,x,d   =as   e/i DurationDemoDistr b,s,p,y,x,d *SCDuration ,s,p,e/i,y,x,d   (Equation 26)
 
     After imputing the durations (block  1126 ), or when the duration imputation director  370  determines there are no site census impressions logged and no DP impressions for the selected episode e/segment z (block  1124 ), the impressions imputation calculator  371  calculates scaled and adjusted impressions (block  1128 ). The impressions imputation calculator  371  receives and/or access the scaled impressions sImp b,s,p,e/i,y,x,d  from the E-S adjuster  324  and the imputed impressions i e/i Imp p,b,s,p,y,x,d  from the impressions imputation director  369 . The impression imputation calculator  371  calculates scaled and adjusted impressions asImp b,s,p,e/i,y,x,d , for each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, by applying (e.g., adding) the imputed impressions i e/i Imp p,b,s,p,y,x,d  to the scaled impressions sImp b,s,p,e/i,y,x,d . Equation 27 below may be used to implement block  1128 .
 
asImp b,s,p,e/i,y,x,d =sImp b,s,p,e/i,y,x,d   *i   e/i   Imp   p,b,s,p,y,x,d   (Equation 27)
 
     The duration imputation calculator  372  calculates scaled and adjusted durations (block  1130 ). The duration imputation calculator  372  receives and/or access the scaled durations sDuration b,s,p,e/i,y,x,d  from the E-S adjuster  324  and the imputed durations i e/i Duration p,b,s,p,y,x,d  from the duration imputation director  370 . The duration imputation calculator  372  calculates scaled and adjusted durations asDuration b,s,p,e/i,y,x,d , for each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, by applying (e.g., adding) the imputed durations i e/i Duration p,b,s,p,y,x,d  to the scaled durations sDuration b,s,p,e/i,y,x,d . Equation 28 below may be used to implement block  1130 .
 
asDuration b,s,p,e/i,y,x,d =sDuration b,s,p,e/i,y,x,d   *i   e/i Duration p,b,s,p,y,x,d   (Equation 28)
 
     The census imputer  326  determines whether there are additional episodes e/segments i (block  1132 ). If the census imputer  326  determines there are additional episodes e/segments i (block  1132 ), the example instructions  1100  return to block  1118 . In this manner, the census imputer  326  determines scaled and adjusted impressions asImp b,s,p,e/i,y,x,d  and durations asDuration b,s,p,e/i,y,x,d  for each episode e/segment z. When the census imputer  326  determines there are no more episodes e/segments i (block  1132 ), the census imputer  326  determines whether there are additional demographic groups d (block  1134 ). 
     When the census imputer  326  determines there are additional demographics d to select (block  1134 ), the example instructions  1100  return to block  1112 . In this manner, the census imputer  326  determines scaled and adjusted impressions asImp b,s,p,e/i,y,x,d  and durations asDuration b,s,p,e/i,y,x,d  for each demographic group d. When the census imputer  326  determines there are no more demographic groups d to select (block  1134 ), the census imputer  326  determines whether there are additional combinations of brand b, sub-brand s, asset p, platform y, and device type x (block  1136 ). 
     When there are additional combinations of brand b, sub-brand s, asset p, platform y, and device type x (block  1136 ), the example instructions  1100  of  FIG.  11    return to block  1112 . In this manner, the census imputer  326  determines scaled and adjusted impressions asImp b,s,p,e/i,y,x,d  and durations asDuration b,s,p,e/i,y,x,d  for each combination of brand b, sub-brand s, asset p, platform y, and device type x. When the census imputer  326  determines there are no additional combinations of brand b, sub-brand s, asset p, platform y, and device type x (block  1136 ), the example instructions  1100  of  FIG.  11    end. 
     Calibrating Segment, Episode, Asset, Sub-Brand, Brand, and Cross-Publisher Level Metrics 
       FIG.  12    is a flow chart representative of example machine readable instructions  1200  which may be executed to implement the audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  to calculate ratings information at the episode level, the first time segment level, and the second time segment level. The metrics generator  328   FIG.  3    may perform the instructions  1200  of  FIG.  12    to estimate impression counts, durations, and unique audiences based on the scaled and adjusted impressions asImp b,s,p,e/i,y,x,d  and durations asDuration b,s,p,e/i,y,x,d  generated by the census imputer  326  and illustrated in  FIG.  11   . 
     Because the metrics generator  328  may execute similar instructions to perform the example instructions  1200  at any of the asset level, the sub-brand level, the brand level, the episode-level and/or the second time segment level, the example instructions  1200  of  FIG.  12    are described below using the asset level, the sub-brand level, the brand level, the episode-level and/or the second time segment level in the alternative (e.g., episode/segment). In some examples, the metrics generator  328  iterates the instructions  1200  beginning at a lower level (e.g., episode level, time segment level) and moving to higher (e.g., broader) levels (e.g., asset level, sub-brand level, brand level) with successive iterations by using the results from the immediately prior iteration for the next iteration. For example, the metrics generator  328  may use the episode/segment level results to execute the instructions  1200  at the asset level, use the asset level results to execute the instructions  1200  at the sub-brand level, and use the sub-brand level results to execute the instructions  1200  at the brand level. The example instructions  1200  are described with reference to the episode/time segment level. 
     The metrics generator  328  obtains the scaled and adjusted impressions and the scaled and adjusted durations from the census imputer (block  1202 ). In examples disclosed herein, the metrics generator  328  obtains the scaled and adjusted impressions asImp b,s,p,e/i,y,x,d  from the impressions imputation calculator  371  and the scaled and adjusted durations asDuration b,s,p,e/i,y,x,d  from the duration imputation calculator  372 . The metrics generator  328  selects a combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, and demographic group d (block  1204 ). The metrics generator  328  subsequently calculates episode-/segment-level impression estimates estImp b,s,p,e/i,y,d  across all device types x (block  1206 ). In examples disclosed herein, the metrics generator  328  calculates segment-level and episode-level impression estimates by summing, or adding together all adjusted and scaled impressions asImp b,s,p,e/i,y,x,d  across all device types x for the selected combination. In some examples, the segment-level impression estimates are performed at the first time segment level. Equation 29 below may be used to implement block  1206 . In Equation 29, n represents the total number of device types x.
 
estImp b,s,p,e/i,y,d =Σ x=1   n asImps b,s,p,e/i,y,x,d   (Equation 29)
 
     The metrics generator  328  calculates episode-/segment-level duration estimates across all device types x (block  1208 ). In examples disclosed herein, the metrics generator  328  calculates segment-level and episode-level duration estimates estDuration b,s,p,e/i,y,d  by summing, or adding together all adjusted and scaled durations asDuration b,s,p,e/i,y,x,d  across all device types x for the selected combination. In some examples, the segment-level duration estimates are performed at the first time segment level. Equation 30 below may be used to implement block  1208 . In Equation 30, n represents the total number of device types x.
 
estDuration b,s,p,e/i,y,d =Σ x=1   n asDuration b,s,p,e/i,y,x,d   (Equation 30)
 
     The metrics generator  328  accesses and/or receives all frequencies and deduplication factors from the distributed demographic data calculator  320  of  FIG.  3   . The metrics generator  328  calculates episode-/segment-level unique audience estimates (block  1210 ). In examples disclosed herein, the metrics generator  328  calculates the episode-level unique audience estimates estUA b,s,p,e,y,d  by dividing, for the selected combination, the episode-level impression estimates estImp b,s,p,e,y,d  by the episode-level frequency rDPFreq b,s,p,e,y,d  of equation 7. Alternatively, the metrics generator  328  calculates the segment-level unique audience estimates estUA b,i,y,d  by dividing, for the selected combination, the segment-level impression estimates estImp b,s,p,i,y,d  by the segment-level frequency rDPFreq b,i,y,d  of equation 10. In some examples, the segment-level unique audience estimates are performed at the second time segment level. Equation 31 below may be used to implement block  1210 .
 
estUA b/(b,s,p),e/i,y,d =estImp b/(b,s,p),e/i,y,d /rDPFreq b/(b,s,p),/i,y,d   (Equation 31)
 
     The metrics generator  328  determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, and demographic d (block  1212 ). When the metrics generator  328  determines there are additional combinations (block  1212 ), the example instructions return to block  1204 . When the metrics generator  328  determines there are no additional combinations (block  1212 ), the metrics generator selects a combination of brand b, sub-brand s, asset p, episode e/segment z, and demographic d (block  1214 ). 
     The metrics generator  328  calculates total digital episode-/segment-level impression estimates across all platforms y (block  1216 ). The metrics generator  328  calculates the total digital episode-/segment-level impression estimates estImp TD,b,s,p,e/i,d  by adding episode-/segment-level impressions across the non-mobile (e.g., desktop) computing platform estImp Desktop,b,s,p,e/i,d  to episode-/segment-level impressions across the mobile computing platform estImp Mobile,b,s,p,e/i,d . In some examples, the total digital segment-level impression estimates are performed at the first time segment level. Equation 32 below may be used to implement block  1216 .
 
estImp TD,b,s,p,e/i,d =estIMP Desktop,b,s,p,e/i,d =estImp Mobile,b,s,p,e/i,d   (Equation 32)
 
     The metrics generator  328  calculates total digital episode-level/segment-level duration estimates across all platforms y (block  1218 ). The metrics generator  328  calculates the total digital episode-level/segment-level duration estimates estDuration TD,b,s,p,e/i,d  by adding episode-level/segment-level durations across the non-mobile (e.g., desktop) computing platform estDuration Desktop,b,s,p,e/i,d  to episode-/segment-level durations across the mobile computing platform estDuration Mobile,b,s,p,e/i,d . In some examples, the total digital segment-level duration estimates are performed at the first time segment level. Equation 33 below may be used to implement block  1218 .
 
estDuration TD,b,s,p,e/i,d =estDuration Desktop,b,s,p,e/i,d +estDuration Mobile,b,s,p,e/i,d   (Equation 33)
 
     The metrics generator  328  calculates total digital episode-/segment-level audience estimates across all platforms y using deduplication factors (block  1220 ). In examples disclosed herein, the metrics generator  328  calculates total digital episode-level audience estimates estUA TDb,s,p,e,d  by adding, for the selected combination of brand b, sub-brand s, asset p, episode e, and demographic group d, episode-level audience estimates across the non-mobile (e.g., desktop) computing platform estUA Desktopb,s,p,e,d  and episode-level audience estimates across the mobile computing platform estUA Mobile,b,s,p,e,d  and multiplying the sum by the episode-level deduplication factor Dedup_F TD,b,s,p,e,d  of equation 8. Alternatively, the metrics generator  328  calculates total digital segment-level audience estimates estUA TD,b,i,d  by adding, for the selected combination of brand b, sub-brand s, asset p, segment z, and demographic group d, segment-level audience estimates across the non-mobile (e.g., desktop) computing platform estUA Desktop,b,i,d  and segment-level audience estimates across the mobile computing platform estUA Mobile,b,i,d  and multiplying the sum by the segment-level deduplication factor Dedup_F TD,b,i,d  of equation 11. In some examples, the segment-level audience estimates are performed at the second time segment level. Equation 34 below may be used to implement block  1220 .
 
estUA TD,b/(b,s,p),e/i,d =(estUA Desktop,b/(b,s,p),e/i,d +estUA Mobile,b/(b,s,p),e/i,d )*Dedup_ F   TD,b/(b,s,p),e/i,d   (Equation 34)
 
     The metrics generator  328  determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, and demographic d (block  1222 ). When there are additional combinations (block  1222 ), the example instructions  1200  return to block  1214 . When there are no additional combinations (block  1222 ), the example instructions  1200  end. 
       FIG.  13    is a flow chart representative of example machine readable instructions  1300  which may be executed to implement the audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  to adjust the ratings information at the first time segment level using impression data at a time sub-segment level. The segment modifier  330  of  FIG.  3    may perform the instructions  1300  of  FIG.  13    to determine impression ratios, duration ratios, and to adjust asset-level impression estimates estImp b,s,p,y,d  (equation 9) and asset-level duration estimates estDuration b,s,p,y,d  (equation 5). 
     In examples disclosed herein, the segment modifier  330  receives segment-level impression estimates estImp b,s,p,i,y,d  (e.g., at the first time segment level) (Equation 29) and segment-level duration estimates estDuration b,s,p,i,y,d  (e.g., at the first time segment level) (Equation 30) from the metrics generator  328  of  FIG.  3   . The segment modifier  330  selects a combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d (block  1302 ). 
     The impression sub-segment adder  373  determines a sum of sub-segments i s  (block  1304 ). In examples disclosed herein, the impression sub-segment adder  373  accesses the segment-level impression estimates estImp b,s,p,i,y,d  and sums, for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, all time sub-segments i s  for a corresponding (e.g., matching the selection) segment-level impression estimate estImp b,s,p,i,y,d  (e.g., all time sub-segments i s  included in the selected segment z). The impression sub-segment adder  373  may determine the sum of the segment-level impression estimates across all time sub-segments i s , referred to herein as the impression sub-segment level sum (e.g., SumEstImp b,s,p,i,y,d ), by analyzing D beacons (e.g., beacon request  226   b  of  FIG.  2   ) associated with (e.g., used to derive) the segment-level impression estimates estImp b,s,p,i,y,d . For example, if a segment level impression estimate estImp b,s,p,i,y,d  collected from a first device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_01010, the impression sub-segment adder  373  identifies the sub-segments i s  (e.g., 01010) and credits the segment as having five sub-segments. Further, if a segment level impression estimate estImp b,s,p,i,y,d  collected from a second device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_00100, the impression sub-segment adder  373  identifies the sub-segments i s  (e.g., 00100) and credits the segment as having five sub-segments. Thus, the impression sub-segment adder  373  may determine the impression sub-segment level sum SumEstImp b,s,p,i,y,d  is ten (e.g., five sub-segments of D_2_01010 plus five sub-segments of D_2_00100). Equation 35 below may be used to implement block  1304 . In Equation 35 below, z(s) is the first sub-segment z s  of the segment z and n is the total number of sub-segments in the segment z.
 
SumEstImp b,s,p,i,y,d =Σ i(s)=1   n estImp b,s,p,i,y,d   (Equation 35)
 
     The duration sub-segment adder  374  determines a duration of sub-segments i s  (block  1306 ). In examples disclosed herein, the duration sub-segment adder  374  accesses the segment-level duration estimates estDuration b,s,p,i,y,d  and sums, for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, durations of all time sub-segments i s  for a corresponding (e.g., matching the selection) segment-level duration estimate estDuration b,s,p,i,y,d  (e.g., summing durations of each time sub-segments i s  included in the selected segment z). The duration sub-segment adder  374  may determine the sum of the segment-level duration estimates across all time sub-segment z s  durations, referred to herein as the duration sub-segment level sum (e.g., SumEstDuration b,s,p,i,y,d ), by analyzing D beacons (e.g., beacon request  226   b  of  FIG.  2   ) associated with (e.g., used to derive) the segment-level duration estimates estDuration b,s,p,i,y,d . For example, if a segment level duration estimate estDuration b,s,p,i,y,d  collected from a first device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_01010, wherein each time sub-segment z s  represents a duration of one minute, the duration sub-segment adder  374  identifies the durations of the sub-segments i s  (e.g., 01010) and credits the segment as having a duration of five minutes. Further, if a segment level duration estimate estDuration b,s,p,i,y,d  collected from a second device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_00100, wherein each time sub-segment z s  represents a duration of one minute, the duration sub-segment adder  374  identifies the durations of the sub-segments i s  (e.g., 00100) and credits the segment as having a duration of five minutes. Thus, the duration sub-segment adder  374  may determine the duration sub-segment level sum SumEstDuration b,s,p,i,y,d  to be ten minutes (e.g., a five minute duration for D_2_01010 plus a five minute duration for D_2_00100). Equation 36 below may be used to implement block  1306 . In Equation 36 below, where i(s) is the first sub-segment z s  duration of the segment z and n is the last sub-segment z s  duration of the segment z.
 
SumEstDuration b,s,p,i,y,d =Σ i(s)=1   n estDuration b,s,p,i,y,d   (Equation 36)
 
     The impression ratio calculator  375  receives the impression sub-segment level sum SumEstImp b,s,p,i,y,d  from the impression sub-segment adder  373 . The impression ratio calculator  375  also receives the segment-level impression estimates estImp b,s,p,i,y,d  (e.g., at the first time segment level) (Equation 29) from the metrics generator  328 . The impression ratio calculator  375  subsequently calculates an impression ratio (block  1308 ). In examples disclosed herein, the impression ratio calculator  375  identifies sub-segments i s  of the segment-level impression estimates estImp b,s,p,i,y,d  that are credited as impressions (e.g., presented via the client device  102  of  FIGS.  1  and/or  2   ). For example, the impression ratio calculator  375  may analyze D beacons (e.g., beacon request  226   b  of  FIG.  2   ) associated with (e.g., used to derive) the segment-level impression estimates estImp b,s,p,i,y,d  and identify a quantity of time sub-segments i s  that are credited as impressions. For example, if the segment-level impression estimate estImp b,s,p,i,y,d  collected from the first device type x (e.g., provided as an example in the aforementioned description of block  1304  (derived from the D beacon D_2_01010)), the impression ratio calculator  375  determines that two time sub-segments were presented (e.g., the 2 nd  time sub-segment and the 4 th  time sub-segment). Thus, the impression ratio calculator  375  determines the quantity of time sub-segments credited as impressions to be two. Further, if the segment-level impression estimate estImp b,s,p,i,y,d  collected from the second device type x (e.g., provided as an example in the aforementioned description of block  1304  (derived from the D beacon D_2_00100)), the impression ratio calculator  375  determines that one time sub-segment was presented (e.g., the 3 rd  time sub-segment). Thus, the impression ratio calculator  375  determines the quantity of time sub-segments credited as impressions to be one. Moreover, the impression calculator  375  determines the quantity of time sub-segments credited as impressions for the selected combination (e.g., est i Imp b,s,p,i,y,d ) to be three (e.g., two time sub-segments presented in D_2_01010 plus one time sub-segment presented in D_2_00100). In examples disclosed herein, the impression ratio calculator  375  calculates the impression ratio EstImpRatio b,s,p,i,y,d  for the selected combination by dividing the quantity of time sub-segments credited as impressions est i Imp b,s,p,i,y,d  by the impression sub-segment level sum SumEstImp b,s,p,i,y,d . As such, the impression ratio calculator  375  calculates an impression ratio of 3/10 or 0.30 by dividing the quantity of time sub-segments credited as impressions est i Imp b,s,p,i,y,d  (e.g., 3) by the impression sub-segment level sum SumEstImp b,s,p,i,y,d  (e.g., 10). Equation 37 below may be used to implement block  1308 .
 
EstImpRatio b,s,p,i,y,d   =est   i   Imp   b,s,p,i,y,d /SumEstImp b,s,p,i,y,d   (Equation 37)
 
     The duration ratio calculator  376  receives the duration sub-segment level sum SumEstDuration b,s,p,i,y,d  from the duration sub-segment adder  374 . The duration ratio calculator  376  also receives the segment-level duration estimates estDuration b,s,p,i,y,d  (e.g., at the first time segment level) (Equation 30) from the metrics generator  328 . The duration ratio calculator  376  subsequently calculates a duration ratio (block  1310 ). In examples disclosed herein, the duration ratio calculator  376  identifies sub-segments i s  of the segment-level duration estimates estDuration b,s,p,i,y,d  that are credited as presented durations (e.g., presented via the client device  102  of  FIGS.  1  and/or  2   ). For example, the duration ratio calculator  376  may analyze D beacons (e.g., the beacon request  226   b  of  FIG.  2   ) associated with (e.g., used to derive) the segment-level duration estimates estDuration b,s,p,i,y,d  and identify a duration of the time sub-segments i s  presented. For example, if the segment-level duration estimate estDuration b,s,p,i,y,d  collected from the first device type x (e.g., provided as an example in the aforementioned description of block  1306  (derived from the D beacon D_2_01010)), the duration ratio calculator  376  determines that two time sub-segments were presented (e.g., the 2 nd  time sub-segment and the 4 th  time sub-segment). Thus, the duration ratio calculator  376  determines the duration of the time sub-segments i s  presented to be two minutes. Further, if the segment-level duration estimate estDuration b,s,p,i,y,d  collected from the second device type x (e.g., provided as an example in the aforementioned description of block  1306  (derived from the D beacon D_2_00100)), the duration ratio calculator  376  determines that one time sub-segment was presented (e.g., the 3 rd  time sub-segment). Thus, the duration ratio calculator  376  determines the duration of the time sub-segments i s  presented to be one minute. Moreover, the duration calculator  376  determines the duration of time sub-segments presented for the selected combination (e.g., est i Duration b,s,p,i,y,d ) to be three minutes (e.g., two minutes for the two time sub-segments presented in D_2_01010 plus one minute for the one time sub-segment presented in D_2_00100). In examples disclosed herein, the duration ratio calculator  376  calculates the duration ratio EstDurationRatio b,s,p,i,y,d  for the selected combination by dividing the duration of time sub-segments presented for the selected combination est i Duration b,s,p,i,y,d  by the duration sub-segment level sum SumEstDuration b,s,p,i,y,d . As such, the duration ratio calculator  376  calculates a duration ratio of 3/10 or 0.30 by dividing the duration of time sub-segments presented est i Duration b,s,p,i,y,d  (e.g., 3 minutes) by the duration sub-segment level sum SumEstDuration b,s,p,i,y,d  (e.g., 10 minutes). Equation 38 below may be used to implement block  1310 .
 
EstDurationRatio b,s,p,i,y,d   =est   i Duration b,s,p,i,y,d /SumEstDuration b,s,p,i,y,d   (Equation 38)
 
     The impression metrics adjuster  377  receives the impression ratio EstImpRatio b,s,p,i,y,d  from the impression ratio calculator  375  and the asset-level impression estimates estImp b,s,p,y,d  (equation 29) (e.g., first asset-level impressions) from the metrics generator  328 . The impression metrics adjuster  377  subsequently adjusts the asset-level impression estimates with the impression ratio (block  1312 ). In examples disclosed herein, the impression metrics adjuster  377  adjusts the asset-level impression estimates estImp b,s,p,y,d  by applying (e.g., multiplying), for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, the impression ratio EstImpRatio b,s,p,i,y,d  to the asset-level impression estimates estImp b,s,p,y,d . As a result, the impression metrics adjuster  377  generates adjusted segment-level impression estimates estImps b,s,p,i,y,d  by applying the impression ratio EstImpRatio b,s,p,i,y,d  to the asset-level impression estimates estImp b,s,p,y,d . Equation 39 below may be used to implement block  1312 .
 
estImps b,s,p,i,y,d =EstImpRatio b,s,p,i,y,d *estImp b,s,p,y,d   (Equation 39)
 
     The duration metrics adjuster  378  receives the duration ratio EstDurationRatio b,s,p,i,y,d  from the duration ratio calculator  376  and the asset-level duration estimates estDuration b,s,p,y,d  (Equation 30) (e.g., first asset-level durations) from the metrics generator  328 . The duration metrics adjuster  378  subsequently adjusts the asset-level duration estimates with the duration ratio (block  1314 ). In examples disclosed herein, the duration metrics adjuster  378  adjusts the asset-level duration estimates estDuration b,s,p,y,d  by applying (e.g., multiplying), for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, the duration ratio EstDurationRatio b,s,p,i,y,d  to the asset-level duration estimates estDuration b,s,p,y,d . As a result, the duration metrics adjuster  378  generates adjusted segment-level duration estimates estDuration b,s,p,i,y,d  by applying the duration ratio EstDurationRatio b,s,p,i,y,d  to the asset-level duration estimates estDuration b,s,p,y,d . Equation 40 below may be used to implement block  1314 .
 
estDuration b,s,p,i,y,d =EstDurationRatio b,s,p,i,y,d *estDuration b,s,p,y,d   (Equation 40)
 
     The segment modifier  330  determines whether there are additional combinations of brand b, sub-brand s, asset p, segment z, platform y, and demographic d (block  1316 ). When the segment modifier  330  determines there are additional combinations (block  1316 ), the example instructions  1300  return to block  1302 . In this manner, the segment modifier  330  generates impression ratios, duration ratios, adjusted segment-level impression estimates, and adjusted segment-level duration estimates across all combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d. When the segment modifier  330  determines there are no additional combinations of brand b, sub-brand s, asset p, segment z, platform y, and demographic d (block  1316 ), the example instructions  1300  of  FIG.  13    end. 
       FIG.  14    is allow chart representative of example machine readable instructions  1400  which may be executed to implement the audience data generator  120  of  FIGS.  1 ,  2   , and/or  3  to adjust the ratings information at the brand level. The brand/cross-platform adjuster  332  of  FIG.  3    may perform the instructions  1400  of  FIG.  14    to estimate impression counts and durations using the adjusted asset-level impression estimates estImps b,s,p,i,y,d  and the adjusted asset-level duration estimates estDuration b,s,p,i,y,d  generated by the segment modifier  330  and illustrated in  FIG.  13   . 
     The brand/cross-platform adjuster  332  accesses and/or receives the adjusted asset-level impression estimates estImps b,s,p,i,y,d  and the adjusted asset-level durations estDuration b,s,p,i,y,d  from the segment modifier  330 . In examples disclosed herein, the brand/cross-platform adjuster  332  also accesses and/or receives the segment level frequency rDPFreq b,i,y,d  (e.g., at the second time segment level) (Equation 10) from the segment FC  341  of the distributed demographic data calculator  320  of  FIG.  3   . The brand/cross-platform adjuster  332  further accesses and/or receives the segment level deduplication factor Dedup_F TD,b,i,d  (Equation 11) from the segment DFG  342 . 
     The brand/cross-platform adjuster  332  selects a combination of brand b, segment z, platform y, and demographic d (block  1402 ). The impressions adjuster  379  subsequently calculates adjusted brand-level impression estimates (Block  1404 ). In examples disclosed herein, the impressions adjuster  379  calculates adjusted brand-level impression estimates estImp b,i,y,d  for the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted asset-level impression estimates estImps b,s,p,i,y,d  across all assets p within sub-brands s. Equation 41 below may be used to implement block  1404 . In the example Equation 41 below, n represents all assets p within a sub-brand s.
 
estImp b,i,y,d =Σ p=1   n aestImps b,s,p,i,y,d   (Equation 41)
 
     The duration adjuster  380  calculates adjusted brand-level duration estimates (Block  1406 ). In examples disclosed herein, the duration adjuster  380  calculates adjusted brand-level duration estimates estDuration b,i,y,d  for the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted asset-level duration estimates estDuration b,s,p,i,y,d  across all assets p within sub-brands s. Equation 42 below may be used to implement block  1406 . In the example Equation 42 below, n represents all assets p within a sub-brands.
 
estDuration b,i,y,d =Σ p=1   n estDuration b,s,p,i,y,d   (Equation 42)
 
     The audience adjuster  381  calculates adjusted brand-level audience estimates (block  1408 ). In examples disclosed herein, the audience adjuster  381  calculates adjusted brand-level audience estimates estUA b,i,y,d  for the selected combination of brand b, segment z, platform y, and demographic d by dividing a respective, adjusted brand-level impression estimate estImp b,i,y,d  by a respective, segment level frequency rDPFreq b,i,y,d  (e.g., at the second time segment level) (Equation 10). Equation 43 may be used to implement block  1408 .
 
estUA b,i,y,d =estImp b,i,y,d /rDPFreq b,i,y,d   (Equation 43)
 
     The brand/cross-platform adjuster  332  determines whether there are additional combinations of brand b, segment z, platform y, and demographic group d (block  1410 ). When the brand/cross-platform adjuster  332  determines there are additional combinations (block  1410 ), the example instructions  1400  return to block  1402 . When the brand/cross-platform adjuster  332  determines there are no additional combinations (block  1410 ), the brand/cross-platform adjuster  332  selects a combination of a brand b, segment z, and demographic d (block  1412 ). 
     The impressions TD calculator  382  determines adjusted total-digital brand-level impression estimates across all platforms y (block  1414 ). In examples disclosed herein, the impressions TD calculator  382  calculates the adjusted total digital brand-level impression estimates estImp TD,b,i,d  by adding adjusted brand-level impression estimates across the non-mobile (e.g., desktop) computing platform estImp Desktop,b,i,d  to adjusted brand-level impression estimates across the mobile computing platform estImp Mobile,b,i,d . Equation 44 below may be used to implement block  1414 .
 
estImp TD,b,i,d =estImp Desktop,b,i,d +estImp Mobile,b,i,d   (Equation 44)
 
     The duration TD calculator  383  determines adjusted total-digital brand-level duration estimates across all platforms y (block  1416 ). In examples disclosed herein, the duration TD calculator  383  calculates the adjusted total digital brand-level duration estimates estDuration TD,b,i,d  by adding adjusted brand-level duration estimates across the non-mobile (e.g., desktop) computing platform estDuration Desktop,b,i,d  adjusted brand-level duration estimates across the mobile computing platform estDuration Mobile,b,i,d . Equation 45 below may be used to implement block  1416 .
 
estDuration TD,b,i,d =estDuration Desktop,b,i,d +estDuration Mobile,b,i,d   (Equation 45)
 
     The audience TD calculator  384  calculates adjusted total digital brand-level audience estimates across all platforms y using deduplication factors (block  1418 ). In examples disclosed herein, the audience TD calculator  384  calculates adjusted total digital brand-level audience estimates estUA TDb,i,d  by adding, for the selected combination of brand b, segment z, and demographic group d, adjusted brand-level audience estimates across the non-mobile (e.g., desktop) computing platform estUA Desktopb,i,d  and adjusted brand-level audience estimates across the mobile computing platform estUA Mobile,b,i,d  and multiplying the sum by the segment level deduplication factor Dedup_F TD,b,i,d  (Equation 11). Equation 46 below may be used to implement block  1418 .
 
estUA TD,b,i,d =(estUA Desktop,b,i,d +estUA Mobile,b,i,d )*Dedup_ F   TD,b,i,d   (Equation 46)
 
     The brand/cross-platform adjuster  332  determines whether there are additional combinations of brand b, segment z, and demographic d (block  1420 ). When there are additional combinations (block  1420 ), the example instructions  1400  return to block  1412 . When there are no additional combinations (block  1420 ), the example instructions  1400  end. 
       FIG.  15    is a flow chart representative of example machine readable instructions  1500  which may be executed to implement the audience data generator  120  of  FIGS.  1 - 3    to calculate ratings information at the cross-publisher level. The brand/cross-platform adjuster  332  of  FIG.  3    may perform the instructions  1500  of  FIG.  15    to estimate impression counts and durations using the adjusted brand-level impression estimates estImps b,i,y,d  and the adjusted brand-level duration estimates estDuration b,i,y,d  generated by the brand/cross-platform adjuster  332 . 
     The brand/cross-platform adjuster  332  accesses and/or receives the adjusted brand-level impression estimates estImps b,i,y,d  and the adjusted brand-level durations estDuration b,i,y,d  from the brand/cross-platform adjuster  332 . In examples disclosed herein, the brand/cross-platform adjuster  332  also accesses and/or receives the cross-publisher level frequency (e.g., rDPCPFreq b,i,y,d ) (Equation 12) from the CP-FC  351  of the distributed demographic data calculator  320  of  FIG.  3   . The brand/cross-platform adjuster  332  further accesses and/or receives the cross-publisher level deduplication factor Dedup_F_CP TD,b,i,d  (Equation 13) from the CP-DFG  352 . 
     The brand/cross-platform adjuster  332  selects a combination of brand b, segment z, platform y, and demographic d (block  1502 ). The impressions adjuster  381  subsequently calculates cross-publisher adjusted brand-level impression estimates (Block  1504 ). In examples disclosed herein, the impressions adjuster  381  calculates cross-publisher adjusted brand-level impression estimates estCPImp b,i,y,d  for the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted brand-level impression estimates estImps b,i,y,d  (Equation 29) across all brands b (e.g., across all network affiliates providing a segment z of the media  206  of  FIG.  2   ). Equation 47 below may be used to implement block  1504 . In example Equation 47 below, n represents all brands b included within a cross-publisher report.
 
estCPImp b,i,y,d =Σ b=1   n estImps b,i,y,d   (Equation 47)
 
     The duration adjuster  380  calculates cross-publisher adjusted brand-level duration estimates (Block  1506 ). In examples disclosed herein, the duration adjuster  380  calculates cross-publisher adjusted brand-level duration estimates estCPDuration b,i,y,d  for the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted brand-level duration estimates estDuration b,i,y,d  (Equation 30) across all brands b (e.g., across all network affiliates providing a segment z of the media  206  of  FIG.  2   ). Equation 48 below may be used to implement block  1506 . In example Equation 48 below, n represents all brands b included within a cross-publisher report.
 
estCPDuration b,i,y,d =Σ b=1   n estDuration b,i,y,d   (Equation 48)
 
     The audience adjuster  381  calculates cross-publisher adjusted brand-level audience estimates (block  1508 ). In examples disclosed herein, the audience adjuster  381  calculates cross-publisher adjusted brand-level audience estimates estCPUA b,i,y,d  for the selected combination of brand b, segment z, platform y, and demographic d by dividing a respective, cross-publisher adjusted brand-level impression estimate estCPImp b,i,y,d  (Equation 47) by a respective, cross-publisher level frequency rDPCPFreq b,i,y,d  (Equation 12). Equation 49 may be used to implement block  1508 .
 
estCPUA b,i,y,d =estCPImp b,i,y,d /rDPCPFreq b,i,y,d   (Equation 49)
 
     The brand/cross-platform adjuster  332  determines whether there are additional combinations of brand b, segment z, platform y, and demographic group d (block  1510 ). When the brand/cross-platform adjuster  332  determines there are additional combinations (block  1510 ), the example instructions  1500  return to block  1502 . When the brand/cross-platform adjuster  332  determines there are no additional combinations (block  1510 ), the brand/cross-platform adjuster  332  selects a combination of a brand b, segment z, and demographic d (block  1512 ). 
     The impressions TD calculator  382  determines cross-publisher total-digital brand-level impression estimates across all platforms y (block  1514 ). In examples disclosed herein, the impressions TD calculator  382  calculates the cross-publisher total digital brand-level impression estimates estCPImp TD,b,i,d  by adding cross-publisher adjusted brand-level impression estimates across the non-mobile (e.g., desktop) computing platform estCPImp Desktop,b,i,d  to cross-publisher adjusted brand-level impression estimates across the mobile computing platform estCPImp Mobile,b,i,d . Equation 50 below may be used to implement block  1514 .
 
estCPImp TD,b,i,d =estCPImp Desktop,b,i,d +estCPImp Mobile,b,i,d   (Equation 50)
 
     The duration TD calculator  383  determines cross-publisher total-digital brand-level duration estimates across all platforms y (block  1516 ). In examples disclosed herein, the duration TD calculator  383  calculates the cross-publisher total digital brand-level duration estimates estCPDuration TD,b,i,d  by adding cross-publisher adjusted brand-level duration estimates across the non-mobile (e.g., desktop) computing platform estCPDuration Desktop,b,i,d  to cross-publisher adjusted brand-level duration estimates across the mobile computing platform estCPDuration Mobile,b,i,d . Equation 51 below may be used to implement block  1516 .
 
estCPDuration TD,b,i,d =estCPDuration Desktop,b,i,d +estCPDuration Mobile,b,i,d   (Equation 51)
 
     The audience TD calculator  384  calculates cross-publisher total digital brand-level audience estimates across all platforms y using deduplication factors (block  1518 ). In examples disclosed herein, the audience TD calculator  384  calculates cross-publisher total digital brand-level audience estimates estCPUA TD,b,i,d  by adding, for the selected combination of brand b, segment z, and demographic group d, cross-publisher adjusted brand-level audience estimates across the non-mobile (e.g., desktop) computing platform estCPUA Desktop,b,i,d  and cross-publisher adjusted brand-level audience estimates across the mobile computing platform estCPUA Mobile,b,i,d  and multiplying the sum by the cross-publisher level deduplication factor Dedup_F_CP TD,b,i,d  (Equation 13). Equation 52 below may be used to implement block  1518 .
 
estCPUA TD,b,i,d =(estCPUA Desktop,b,i,d +estCPUA Mobile,b,i,d )*Dedup_ F _ CP   TD,b,i,d   (Equation 52)
 
     The brand/cross-platform adjuster  332  determines whether there are additional combinations of brand b, segment z, and demographic d (block  1520 ). When there are additional combinations (block  1520 ), the example instructions  1500  return to block  1512 . When there are no additional combinations (block  1520 ), the example instructions  1500  end. 
       FIG.  16    is a flow chart representative of example machine readable instructions  1600  which may be executed to implement the audience data generator  120  of  FIGS.  1 - 3    to generate ratings information by media content type c at the text and video (T/V) segment level. The content calculator  336  of  FIG.  3    may perform the instructions  1600  of  FIG.  16    to estimate a corrected audience count using the redistributed impression information organized by content type c. 
     The content calculator  336  accesses and/or receives the redistributed impressions rDPImps c(t,v,w),b,i,y,d  (Equation 4), the redistributed durations rDPDuration c(t,v,w),b,i,y,d  (Equation 5), and the redistributed audience rDPUA c(t,v,w),b,i,y,d  (Equation 6) from the demographic distributor  318 . The content calculator  336  also accesses and/or receives the adjusted brand-level impression estimates estImp b,i,y,d  (Equation 29) from the brand/cross-platform adjuster  332 . 
     The content calculator  336  selects a combination of brand b, segment z, platform y, and demographic d (block  1602 ). The T/V DFG  391  subsequently calculates a text and video deduplication factor (block  1604 ). In examples disclosed herein, the T/V DFG  391  identifies, for the selected combination of brand b, segment z, platform y, and demographic d, the redistributed audience presented both the text content and the video content w, rDPUA w,b,i,y,d , the redistributed audience presented the video content v, rDPUA v,b,i,y,d , and the redistributed audience presented the text content t, rDPUA t,b,i,y,d . In examples disclosed herein, the T/V DFG  391  calculates a text and video (T/V) deduplication factor (e.g., T&amp;V_Dedup_F b,i,y,d ) by dividing the redistributed audience presented both the text content and the video content w, rDPUA w,b,i,y,d , by a sum of the redistributed audience presented the video content v, rDPUA v,b,i,y,d , and the redistributed audience presented the text content t, rDPUA t,b,i,y,d . Equation 53 below may be used to implement block  1604 .
 
 T &amp; V _Dedup_ F   b,i,y,d =rDPUA w,b,i,y,d /(rDPUA v,b,i,y,d +rDPUA t,b,i,y,d )  (Equation 53)
 
     The T/V audience calculator  392  subsequently calculates a total content audience (block  1606 ). In examples disclosed herein, the T/V audience calculator  392  determines the total content audience (e.g., estUA TC   b,i,y,d ), referred to herein as a corrected audience count, using the adjusted brand-level impression estimates estImp b,i,y,d  (Equation 29). The T/V audience calculator  392  receives the text and video (T/V) deduplication factor T&amp;V_Dedup_F b,i,y,d  from the T/V DFG  391 . The T/V audience calculator  392  calculates the total content audience estUA TC   b,i,y,d , for the selected brand b, segment z, platform y, and demographic d, by summing a video content audience estUA Video   b,i,y,d  and a text content audience estUA Text   b,i,y,d  and applying (e.g., multiplying) the sum by the text and video (T/V) deduplication factor T&amp;V_Dedup_F b,i,y,d . In examples disclosed herein, the video content audience estUA Video   b,i,y,d  is representative of a quantity of audience members of a demographic group d (e.g., M13-17, M40-44, F25-29, etc.) that accessed and were presented a segment z of video content v via a particular computing platform y (e.g., non-mobile or mobile) from a brand b (e.g., a network affiliate providing the media  206  of  FIG.  2   ). The text content audience estUA Text   b,i,y,d  is representative of a quantity of audience members of the same demographic group d that accessed and were concurrently presented text content t when the segment z of the video content v was presented via the same computing platform y from the same brand b. Equation 54 may be used to implement block  1606 .
 
estUA TC   b,i,y,d =(estUA Video   b,i,y,d +estUA Text   b,i,y,d )* T &amp; V _Dedup_ F   b,i,y,d   (Equation 54)
 
     In examples disclosed herein, the T/V audience calculator  392  may determine the video content audience estUA Video   b,i,y,d  and the text content audience estUA Text   b,i,y,d  using the redistributed impressions indicative of access to text content t and video content v, rDPImps T/V,b,i,y,d , the redistributed audiences indicative of access to text content t and video content v, rDPUA T/V,b,i,y,d , and the adjusted brand-level impression estimates estImp b,i,y,d . For example, the T/V audience calculator  392  may first calculate a video impression frequency rDPFreq Video   b,i,y,d  by dividing, for the selected combination of brand b, segment z, platform y, and demographic d of block  1602 , the redistributed impressions indicative of access to video content v, rDPImps v,b,i,y,d , by the redistributed audience indicative of access to the video content v, rDPUA v,b,i,y,d . Equation 55 below may be used to calculate the video impression frequency rDPFreq Video   b,i,y,d .
 
rDPFreq Video   b,i,y,d =rDPImps v,b,i,y,d /rDPUA v,b,i,y,d   (Equation 55)
 
     Similarly, the T/V audience calculator  392  may also calculate a text impression frequency rDPFreq Text   b,i,y,d  by dividing, for the selected combination of brand b, segment z, platform y, and demographic d of block  1602 , the redistributed impressions indicative of access to text content t, rDPImps t,b,i,y,d , by the redistributed audience indicative of access to the text content t, rDPUA t,b,i,y,d . Equation 56 below may be used to calculate the text impression frequency rDPFreq Text   b,i,y,d .
 
rDPFreq Text   b,i,y,d =rDPImps t,b,i,y,d /rDPUA t,b,i,y,d   (Equation 56)
 
     To determine the video content audience estUA Video   b,i,y,d , the T/V audience calculator  392  divides, for the selected combination of brand b, segment z, platform y, and demographic d of block  1602 , the adjusted brand-level impression estimates estImp b,i,y,d  by the video impression frequency rDPFreq Video   b,i,y,d . Equation 57 below may be used to calculate the video content audience used in equation 54 and implemented in block  1606 .
 
estUA Video   b,i,y,d =estImp b,i,y,d /rDPFreq Video   b,i,y,d   (Equation 57)
 
     Similarly, to determine the text content audience estUA Text   b,i,y,d , the T/V audience calculator  392  divides, for the selected combination of brand b, segment z, platform y, and demographic d of block  1602 , the adjusted brand-level impression estimates estImp b,i,y,d  by the text impression frequency rDPFreq Text   b,i,y,d . Equation 58 below may be used to calculate the text content audience used in equation 54 and implemented in block  1606 .
 
estUA Text   b,i,y,d =estImp b,i,y,d /rDPFreq Text   b,i,y,d   (Equation 58)
 
     The content calculator  336  determines whether there are additional combinations of brand b, segment z, platform y, and demographic d (block  1608 ). When the content calculator  336  determines there are additional combinations (block  1608 ), the example instructions  1600  return to block  1602 . In this manner, the content calculator  336  determines a T/V deduplication factor and corrected audience counts for each combination of brand b, segment z, platform y, and demographic group d. When the content calculator  336  determines there are no additional combinations (block  1608 ), the content calculator  336  selects a combination of brand b, segment z, and demographic d (block  1610 ). 
     The TD DFG  393  then calculates a total digital, text and video deduplication factor (block  1612 ). In examples disclosed herein, the TD DFG  393  calculates the total digital, text and video deduplication factor T&amp;V_Dedup_F TD,b,i,d  for text and video (T/V) segment level demographic data provided to the audience data generator  120   a  by the DP  116 . The TD DFG  393  calculates the total digital, text and video deduplication factor T&amp;V_Dedup_F TD,b,i,d  by dividing, for the selected combination of brand b, segment z, and demographic d, a redistributed audience across all content types c (e.g., text t, video v, and both text and video w) that is attributed to the total digital platform r c DPUA TD,b,i,d  by a sum of a redistributed audience across all content types c (e.g., text t, video v, and both text and video w) that is attributed to the non-mobile (e.g., desktop) computing platform r c DPUA Desktop,b,i,d  and a redistributed audience across all content types (e.g., text t, video v, and both text and video w) that is attributed to the mobile computing platform r c DPUA Mobile,b,i,d . Equation 59 below may be used to implement block  1612 .
 
 T &amp; V _Dedup_ F   TD,b,i,d   =r   c DPUA TD,b,i,d /( r   c DPUA Desktop,b,i,d   +r   c DPUA Mobile,b,i,d )  (Equation 59)
 
     The TD audience calculator  394  calculates a total digital audience (block  1614 ). In examples disclosed herein, the TD audience calculator  394  receives the total digital, text and video deduplication factor T&amp;V_Dedup_F TD,b,i,d  from the TD DFG  393 . The TD audience calculator  394  also accesses and/or receives the adjusted brand-level impression estimates estImp b,i,y,d  (Equation 29). The TD audience calculator  394  calculates the total digital, total content audience estUA TC   TD,b,i,d , for the selected brand b, segment z, and demographic d, by summing a total digital video content audience estUA Video   TD,b,i,d  and a total digital text content audience estUA Text   TD,b,i,d  and applying (e.g., multiplying) the sum by the total digital, text and video (T/V) deduplication factor T&amp;V_Dedup_F TD,b,i,d . In examples disclosed herein, the total digital video content audience estUA Video   TD,b,i,d  is representative of a quantity of audience members of a demographic group d (e.g., M13-17, M40-44, F25-29, etc.) that accessed and were presented a segment z of video content v via both the non-mobile computing platform and the mobile computing platform from a brand b (e.g., a network affiliate providing the media  206  of  FIG.  2   ). The total digital text content audience estUA Text   TD,b,i,d  is representative of a quantity of audience members of the same demographic group d that accessed and were concurrently presented text content t when the segment z of the video content v was presented via the mobile computing platform and the non-mobile computing platform from the same brand b. Equation 60 may be used to implement block  1616 .
 
estUA TC   TD,b,i,d =(estUA Video   TD,b,i,d +estUA Text   TD,b,i,d )* T &amp; V _Dedup_ F   TD,b,i,d   (Equation 60)
 
     In examples disclosed herein, the TD audience calculator  394  may determine the total digital video content audience estUA Video   TD,b,i,d  and the total digital text content audience estUA Text   TD,b,i,d  using the redistributed impressions indicative of access to text content t and video content v across all platforms y, rDPImps T/V,b,i,d , the redistributed audiences indicative of access to text content t and video content v across all platforms y, rDPUA T/V,b,i,d , and the adjusted brand-level impression estimates across all platforms y estImp b,i,d . For example, the TD audience calculator  394  may first calculate a total digital video impression frequency rDPFreq Video   TD,b,i,d  by dividing, for the selected combination of brand b, segment z, and demographic d of block  1610 , the redistributed impressions indicative of access to video content v across all platforms y, rDPImps v,b,i,d , by the redistributed audience indicative of access to the video content v across all platforms y, rDPUA v,b,i,d . Equation 61 below may be used to calculate the total digital video impression frequency rDPFreq Video   TD,b,i,d .
 
rDPFreq Video   TD,b,i,d =rDPImps v,b,i,d /rDPUA v,b,i,d   (Equation 61)
 
     Similarly, the TD audience calculator  394  may also calculate a total digital text impression frequency rDPFreq Text   TD,b,i,d  by dividing, for the selected combination of brand b, segment z, and demographic d of block  1610 , the redistributed impressions indicative of access to text content t across all platforms y, rDPImps t,b,i,d , by the redistributed audience indicative of access to the text content t across all platforms y, rDPUA t,b,i,d . Equation 62 below may be used to calculate the total digital text impression frequency rDPFreq Text   TD,b,i,d .
 
rDPFreq Text   TD,b,i,d =rDPImps t,b,i,d /rDPUA t,b,i,d   (Equation 62)
 
     To determine the total digital video content audience estUA Video   TD,b,i,d , the TD audience calculator  394  divides, for the selected combination of brand b, segment z, and demographic d of block  1610 , the adjusted brand-level impression estimates across all platforms estImp b,i,d  by the total digital video impression frequency rDPFreq Video   TD,b,i,d . Equation 63 below may be used to calculate the total digital video content audience used in equation 63 and implemented in block  1614 .
 
estUA Video   TD,b,i,d =estImp b,i,d /rDPFreq Video   TD,b,i,d   (Equation 63)
 
     Similarly, to determine the total digital text content audience estUA Text   TD,b,i,d , the TD audience calculator  394  divides, for the selected combination of brand b, segment z, and demographic d of block  1610 , the adjusted brand-level impression estimates across all platforms y estImp b,i,d  by the total digital text impression frequency rDPFreq Text   TD,b,i,d . Equation 64 below may be used to calculate the total digital text content audience used in equation 60 and implemented in block  1614 .
 
estUA Text   TD,b,i,d =estImp b,i,d /rDPFreq Text   TD,b,i,d   (Equation 64)
 
     The content calculator  336  determines whether there are additional combinations of brand b, segment z, and demographic d (block  1616 ). When the content calculator  336  determines there are additional combinations (block  1616 ), the example instructions  1600  return to block  1610 . When the content calculator  336  determines there are no additional combinations (block  1616 ), the example instructions  1600  of  FIG.  16    end. 
     In some examples, the corrected audience counts calculated by the T/V audience calculator  392  in the illustrated example of  FIG.  16    may be used to generating ratings information for the video. For example, the ratings data generator  338  may generate ratings information for the video by subtracting the corrected audience counts estUA TC   b,i,y,d  from the text and video (T/V) segment level demographic data provided to the audience data generator  120   a  by the DP  116  (e.g., the audience counts provided by the DP  116  before redistribution by the demographic distributor  318 ). In this manner, the audience data generator  120  may determine a DP duplicated audience count (e.g., an overcompensating number of unique audience members identified by the DP  116  that were credited as being presented text content t and video content v). 
       FIG.  17    is a block diagram of an example processor platform  1700  capable of executing the instructions of  FIGS.  4 - 7  and  9 - 16    to implement the audience data generator  120  of  FIGS.  1 ,  2   , and/or  3 . The processor platform  1700  can be, for example, a server, a personal computer, or any other type of computing device. 
     The processor platform  1700  of the illustrated example includes a processor  1712 . The processor  1712  of the illustrated example is hardware. For example, the processor  1712  can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. 
     The example processor  1712  of  FIG.  17    may implement the example calibration data collector  300 , the example platform data manager  302 , the example device data manager  304 , the example segment data manager  306 , the example episode data manager  308 , the example asset data manager  310 , the example sub-brand data manager  312 , the example brand data manager  314 , the example database proprietor (DP) data manager  316 , the example demographic distributor  318 , the example distributed demographic data calculator  320 , the example matrix generator  322 , the example episode-segment (E-S) adjuster  324 , the example census imputer  326 , the example metrics generator  328 , the example segment modifier  330 , the example brand/cross-platform adjuster  332 , the example content calculator  336 , the example ratings data generator  338 , the example duration-per-impression (DPI) calculator  340 , the example segment frequency calculator (FC)  341 , the example segment deduplication factor generator (DFG)  342 , the example episode frequency calculator (FC)  343 , the example episode deduplication factor generator (DFG)  344 , the example asset frequency calculator (FC)  345 , the example asset deduplication factor generator (DFG)  346 , the example sub-brand frequency calculator (FC)  347 , the example sub-brand deduplication factor generator (DFG)  348 , the example brand frequency calculator (FC)  349 , the example brand deduplication factor generator (DFG)  350 , the example cross publisher frequency calculator (CP-FC)  351 , the example cross publisher deduplication factor generator (CP-DFG)  352 , the example matrix converter  353 , the example impressions sharing adjuster  354 , the example duration sharing adjuster  355 , the example coverage vector calculator  356 , the example impressions coverage adjuster  357 , the example duration coverage adjuster  358 , the example impression scaling factor generator  359 , the example duration scaling factor generator  360 , the example impressions scaler  361 , the example duration scaler  362 , the example impressions adder  363 , the example duration adder  364 , the example impressions imputation adder  365 , the example duration imputation adder  366 , the example impressions distribution deriver  367 , the example duration distribution deriver  368 , the example impressions imputation director  369 , the example duration imputation director  370 , the example impressions imputation calculator  371 , the example duration imputation calculator  372 , the example impression sub-segment adder  373 , the example duration sub-segment adder  374 , the example impression ratio calculator  375 , the example duration ratio calculator  376 , the example impression metrics adjuster  377 , the example duration metrics adjuster  378 , the example impressions adjuster  379 , the example duration adjuster  380 , the example audience adjuster  381 , the example impressions total digital (TD) calculator  382 , the example duration total digital (TD) calculator  383 , the example audience total digital (TD) calculator  384 , the example text and video (T/V) deduplication factor generator (DFG)  391 , the example text and video (T/V) audience calculator  392 , the example total digital (TD) deduplication factor generator  393 , the example total digital (TD) audience calculator  394  and/or, more generally, the example audience data generator  120  of  FIGS.  1 ,  2   , and/or  3 . 
     The processor  1712  of the illustrated example includes a local memory  1713  (e.g., a cache). The processor  1712  of the illustrated example is in communication with a main memory including a volatile memory  1714  and a non-volatile memory  1716  via a bus  1718 . The volatile memory  1714  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  1716  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  1714 ,  1716  is controlled by a memory controller. 
     The processor platform  1700  of the illustrated example also includes an interface circuit  1720 . The interface circuit  1720  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface. 
     In the illustrated example, one or more input devices  1722  are connected to the interface circuit  1720 . The input device(s)  1722  permit(s) a user to enter data and commands into the processor  1712 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  1724  are also connected to the interface circuit  1720  of the illustrated example. The output devices  1724  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, a printer and/or speakers). The interface circuit  1720  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor. 
     The interface circuit  1720  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  1726  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  1700  of the illustrated example also includes one or more mass storage devices  1728  for storing software and/or data. Examples of such mass storage devices  1728  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     The coded instructions  1732  of  FIGS.  4 - 7  and  9 - 16    may be stored in the mass storage device  1728 , in the volatile memory  1714 , in the non-volatile memory  1716 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     From the foregoing, it will appreciate that the above disclosed methods, apparatus and articles of manufacture provide a solution to the problem of inaccuracies due to techniques used in online audience measurement. Benefits to online audience measurement, which is an inherently network-based technology, obtained from disclosed example methods, apparatus, and articles of manufacture include a reduction in required network communications that would be necessary to attribute to demographic groups those impressions that are not identifiable by one or more database proprietors. For example, disclosed examples avoid transmitting queries to secondary database proprietors and/or reduce processing and network resources used by the mobile devices as part of the online audience measurement techniques. At the same time, the improved accuracy of ratings information that can be generated using disclosed examples permits a more efficient and more beneficial distribution of advertising resources to viewers of online media by providing rapid, and, more importantly, accurate online audience measurement that enables advertisers to change distributions of advertising resources in response to audience measurement information. 
     Disclosed examples also improve the accuracy of audience measurement for Internet-delivered media such as streaming videos by correcting for measurement errors arising from problems inherent to computer networks. For example, beacon requests and/or other messages described herein can be dropped or otherwise not delivered to the intended destination (e.g., audience measurement entity, a database proprietor, etc.), which in at least some instances can lead to non-negligible measurement bias. 
     Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.