Patent Publication Number: US-2023132878-A1

Title: Methods and apparatus to analyze and adjust demographic information

Description:
RELATED APPLICATIONS 
     This patent arises from a continuation of U.S. patent application Ser. No. 17/163,533, filed Jan. 31, 2021, which is a continuation of U.S. patent application Ser. No. 16/051,055, filed Jul. 31, 2018, now U.S. Pat. No. 10,909,559, which is a continuation of U.S. patent application Ser. No. 15/420,861, filed Jan. 31, 2017, now U.S. Pat. No. 10,096,035, which is a continuation of U.S. patent application Ser. No. 14/809,888, filed on Jul. 27, 2015, now U.S. Pat. No. 9,582,809, which is a continuation of U.S. patent application Ser. No. 13/209,292, filed on Aug. 12, 2011, now U.S. Pat. No. 9,092,797, which claims priority to U.S. Provisional Patent Application No. 61/385,553, filed on Sep. 22, 2010, and U.S. Provisional Patent Application No. 61/386,543, filed on Sep. 26, 2010, all of which are hereby incorporated herein by reference in their entireties. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure relates generally to audience measurements and, more particularly, to methods and apparatus to analyze and adjust demographic information of audience members. 
     BACKGROUND 
     Traditionally, audience measurement entities determine audience compositions for media programming by monitoring on 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 programs (e.g., television programs or radio programs, movies, DVDs, online behavior, etc.) exposed to those panel members. In this manner, the audience measurement entity can identify demographic markets for which impressions or exposures to different media content occur. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example apparatus that may be used to generate an adjustment model to adjust demographic information of audience members. 
         FIG.  2    illustrates an initial age scatter plot of baseline self-reported ages from a social media website prior to adjustment versus highly reliable panel reference ages. 
         FIG.  3    illustrates an example manner of using an adjustment model from  FIG.  1    to analyze and/or adjust demographic information of audience members. 
         FIGS.  4 A- 4 C  show a raw demographic and behavioral variables table to store variables indicative of different demographic and/or behavioral data collected for panelists of the audience measurement entity of  FIG.  1    and/or registered users of a database proprietor of  FIG.  1   . 
         FIGS.  5 A and  5 B  show a recoded demographic and behavioral variables table to store recoded variables indicative of different demographic and/or behavioral data collected for the panelists of the audience measurement entity of  FIG.  1    and/or the registered users of the database proprietor of  FIG.  1   . 
         FIG.  6    shows an example audience measurement entity (AME) age category table. 
         FIG.  7    shows an example terminal node table showing tree model predictions for multiple leaf nodes of a classification tree. 
         FIGS.  8 A and  8 B  are a flow diagram representative of example machine readable instructions that may be executed to generate an adjustment model, to analyze demographic data based on the adjustment model, and/or to adjust demographic data. 
         FIG.  9    illustrates an example processor system that may be used to execute the example instructions of  FIGS.  8 A and  8 B  to implement the example apparatus of  FIG.  1   . 
         FIG.  10    is a communication flow diagram of an example manner in which a web browser can report ad impressions to servers having access to demographic information for a user of that web browser. 
         FIG.  11    is a flow diagram representative of example machine readable instructions that may be executed by a panelist monitoring system to log demographics-based advertisement impressions and/or redirect beacon requests to web service providers to log demographics-based advertisement impressions. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods, apparatus, systems, and/or articles of manufacture disclosed herein may be used to analyze and adjust demographic information of audience members. Examples disclosed herein may be used for online audience measurements in which user-exposures to online content is monitored or measured. Web-based services or Internet-based services often require users to register in exchange for access to such services. Such registration processes elicit demographic information (e.g., gender, age, etc.) from users. The web-based or Internet-based services store the collected demographic information and, as such, the web-based or Internet-based services are referred to herein as demographic database proprietors (i.e., database proprietors). A database proprietor may be, for example, any entity that requests user information from users in exchange for access to online services such as Facebook, Google, Yahoo!, MSN, Twitter, Apple iTunes, Experian, etc. For online audience measurement processes, the collected demographic information may be used to identify different demographic markets to which online content exposures are attributable. 
     A problem facing online audience measurement processes is that the manner in which registered users represent themselves to online data proprietors is not necessarily veridical (e.g., accurate). Example approaches to online measurement that leverage account registrations at such online database proprietors to determine demographic attributes of an audience may lead to inaccurate demographic-exposure results if they rely on self-reporting of personal/demographic information by the registered users during account registration at the database proprietor site. There may be numerous reasons for why users report erroneous or inaccurate demographic information when registering for database proprietor services. The self-reporting registration processes used to collect the demographic information at the database proprietor sites (e.g., social media sites) does not facilitate determining the veracity of the self-reported demographic information. 
     Examples disclosed herein overcome inaccuracies often found in self-reported demographic information found in the data of database proprietors (e.g., social media sites) by analyzing how those self-reported demographics from one data source (e.g., online registered-user accounts maintained by database proprietors) relate to reference demographic information of the same users collected by more reliable means (e.g., in-home or telephonic interviews conducted by the audience measurement entity as part of a panel recruitment process). In examples disclosed herein, an audience measurement entity (AME) collects reference demographic information for a panel of users (e.g., panelists) using highly reliable techniques (e.g., employees or agents of the AME telephoning and/or visiting panelist homes and interviewing panelists) to collect highly accurate information. In addition, the AME installs online meters at panelist computers to monitor exchanges between the metered computers of the panelists and servers of the database proprietors known to have the self-reported demographic information of the panelists. With cooperation by the database proprietors, the AME uses the collected monitoring data to link the panelist reference demographic information maintained by the AME to the self-reported demographic information maintained by the database proprietors on a per-person basis and to model the relationships between the highly accurate reference data collected by the AME and the self-report demographic information collected by the database proprietor (e.g., the social media site) to form a basis for adjusting or reassigning self-reported demographic information of other users of the database proprietor that are not in the panel of the AME. In this manner, the accuracy of self-reported demographic information can be improved when demographic-based online media-impression measurements are compiled for non-panelist users of the database proprietor(s). 
     A scatterplot  200  of baseline self-reported ages taken from a database of a database proprietor prior to adjustment versus highly reliable panel reference ages is shown in  FIG.  2   . The scatterplot  200  shows a clearly non-linear skew in the error distribution. This skew is in violation of the regression assumption of normally distributed residuals (i.e., systematic variance), which would lead to limited success when analyzing and adjusting self-reported demographic information using known linear approaches (e.g., regression, discriminant analysis). For example, such known linear approaches could introduce inaccurate bias or shift in demographics resulting in inaccurate conclusions. Unlike such linear approaches, examples disclosed herein do not generalize the entire dataset to a single function. In some such examples, classification, or tree-based, approaches are used to recursively split datasets into successively smaller and distinct groups based on which independent variables can account for the statistically strongest division. In examples disclosed herein, such independent variables are based on online user behavior such as, for example, quantities of user connections (e.g., online friends), quantities of mobile page views, year of school graduation, median year of school graduation for persons corresponding to the user connections, and a percent of friends which are female. The classification, or tree-based, approaches based on independent variables facilitate first segmenting the demographic data on the basis of behavioral variables and demographics to assess the degree of demographic matches within each distinct group (e.g., behavior-based groups) and then applying adjustments only to demographic data in need of correction, rather than affecting an entire distribution as would otherwise be done using known linear approaches. 
     Some disclosed example methods, apparatus, systems, and articles of manufacture to analyze and adjust demographic information of audience members involve generating a first model based on reference demographic data corresponding to panelists and based on second demographic data and behavioral data from a database proprietor. In some examples, the second demographic data and the behavioral data corresponding to ones of the panelists having user accounts with the database proprietor. Disclosed example methods also involve using the first model to partition the second demographic data into a plurality of nodes, each node representing a respective subset of the second demographic data. In addition, at least some of the second demographic data is redistributed between at least some of the nodes to generate a second model. 
     In some examples, the behavioral data includes at least one of a quantity of user connections (e.g., online friends), a quantity of mobile webpage views, an indicated year of school graduation, a median year of school graduation for persons corresponding to the user connections, and a percent of friends that are female. In some examples, the database proprietor provides a social networking service to registered users including non-panelists and ones of the panelists having user accounts with the database proprietor. 
     In some examples, the second model is applied to third demographic data at the database proprietor and a statistical analysis is performed on the output of the second model to determine whether to adjust at least some of the third demographic data based on the statistical analysis. In some examples, the third demographic data corresponds to registered users of the database proprietor. In some examples, some of the registered users include non-panelists. In some examples, the third demographic data corresponds to users for which impressions to advertisements are logged when the advertisements are rendered on computers of the users. 
       FIG.  1    illustrates an example apparatus  100  that may be used to model, analyze, and/or adjust demographic information of audience members in accordance with the teachings of this disclosure. The apparatus  100  of the illustrated example includes a data interface  102 , a modeler  104 , an analyzer  106 , an adjuster  108 , the training models  128 , and the adjustment model  132 . While an example manner of implementing the apparatus  100  has been illustrated in  FIG.  1   , one or more of the elements, processes and/or devices illustrated in  FIG.  1    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the data interface  102 , the modeler  104 , the analyzer  106 , the adjuster  108 , the training models  128 , the adjustment model  132  and/or, more generally, the example apparatus  100  of  FIG.  1    may be implemented by hardware, software, firmware and/or any combination of hardware, software, and/or firmware. Thus, for example, any of the data interface  102 , the modeler  104 , the analyzer  106 , the adjuster  108 , the training models  128 , and the adjustment model  132  and/or, more generally, the example apparatus  100  could be implemented by one or more circuit(s), 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)), etc. When any of the appended apparatus or system claims are read to cover a purely software and/or firmware implementation, at least one of the data interface  102 , the modeler  104 , the analyzer  106 , the adjuster  108 , the training models  128 , and/or the adjustment model  132  is hereby expressly defined to include a tangible computer readable medium such as a memory, DVD, CD, etc. storing the software and/or firmware. Further still, the example apparatus  100  of  FIG.  1    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  1   , and/or may include more than one of any or all of the illustrated elements, processes and/or devices. 
     To obtain panel reference demographic data, self-reporting demographic data, and user online behavioral data, the example apparatus  100  is provided with the data interface  102 . In the illustrated example of  FIG.  1   , the data interface  102  obtains reference demographics data  112  from a panel database  114  of an AME  116  storing highly reliable demographics information of panelists registered in one or more panels of the AME  116 . In the illustrated example, the reference demographics information  112  in the panel database  114  is collected from panelists by the AME  116  using techniques which are highly reliable (e.g., in-person and/or telephonic interviews) for collecting highly accurate and/or reliable demographics. In the examples disclosed herein, panelists are persons recruited by the AME  116  to participate in one or more radio, movie, television and/or computer panels that are used to track audience activities related to exposures to radio content, movies, television content, computer-based media content, and/or advertisements on any of such media. 
     In addition, the data interface  102  of the illustrated example also retrieves self-reported demographics data  118  and/or behavioral data  120  from a user accounts database  122  of a database proprietor (DBP)  124  storing self-reported demographics information of users, some of which are panelists registered in one or more panels of the AME  116 . In the illustrated example, the self-reported demographics data  118  in the user accounts database  122  is collected from registered users of the database proprietor  124  using, for example, self-reporting techniques in which users enroll or register via a webpage interface to establish a user account to avail themselves of web-based services from the database proprietor  124 . The database proprietor  124  of the illustrated example may be, for example, a social network service provider, an email service provider, an internet service provider (ISP), or any other web-based or Internet-based service provider that requests demographic information from registered users in exchange for their services. For example, the database proprietor  124  may be any entity such as Facebook, Google, Yahoo!, MSN, Twitter, Apple iTunes, Experian, etc. Although only one database proprietor is shown in  FIG.  1   , the AME  116  may obtain self-reported demographics information from any number of database proprietors. 
     In the illustrated example, the behavioral data  120  (e.g., user activity data, user profile data, user account status data, user account data, etc.) may be, for example, graduation years of high school graduation for friends or online connections, quantity of friends or online connections, quantity of visited web sites, quantity of visited mobile web sites, quantity of educational schooling entries, quantity of family members, days since account creation, ‘.edu’ email account domain usage, percent of friends or online connections that are female, interest in particular categorical topics (e.g., parenting, small business ownership, high-income products, gaming, alcohol (spirits), gambling, sports, retired living, etc.), quantity of posted pictures, quantity of received and/or sent messages, etc. 
     In examples disclosed herein, a webpage interface provided by the database proprietor  124  to, for example, enroll or register users presents questions soliciting demographic information from registrants with little or no oversight by the database proprietor  124  to assess the veracity, accuracy, and/or reliability of the user-provided, self-reported demographic information. As such, confidence levels for the accuracy or reliability of self-reported demographics data stored in the user accounts database  122  are relatively low for certain demographic groups. There are numerous social, psychological, and/or online safety reasons why registered users of the database proprietor  124  inaccurately represent or even misrepresent demographic information such as age, gender, etc. 
     In the illustrated example, the self-reported demographics data  118  and the behavioral data  120  correspond to overlapping panelist-users. Panelist-users are hereby defined to be panelists registered in the panel database  114  of the AME  116  that are also registered users of the database proprietor  124 . The apparatus  100  of the illustrated example models the propensity for accuracies or truthfulness of self-reported demographics data based on relationships found between the reference demographics  112  of panelists and the self-reported demographics data  118  and behavioral data  120  for those panelists that are also registered users of the database proprietor  124 . 
     To identify panelists of the AME  116  that are also registered users of the database proprietor  124 , the data interface  102  of the illustrated example can work with a third party that can identify panelists that are also registered users of the database proprietor  124  and/or can use a cookie-based approach. For example, the data interface  102  can query a third-party database that tracks persons that have registered user accounts at the database proprietor  124  and that are also panelists of the AME  116 . Alternatively, the data interface  102  can identify panelists of the AME  116  that are also registered users of the database proprietor  124  based on information collected at web client meters installed at panelist client computers for tracking cookie IDs for the panelist members. In this manner, such cookie IDs can be used to identify which panelists of the AME  116  are also registered users of the database proprietor  124 . In either case, the data interface  102  can effectively identify all registered users of the database proprietor  124  that are also panelists of the AME  116 . 
     After distinctly identifying those panelists from the AME  116  that have registered accounts with the database proprietor  124 , the data interface  102  queries the user account database  122  for the self-reported demographic data  118  and the behavioral data  120 . In addition, the data interface  102  compiles relevant demographic and behavioral information into a panelist-user data table  126 . In some examples, the panelist-user data table  126  may be joined to the entire user base of the database proprietor  124  based on, for example, cookie values, and cookie values may be hashed on both sides (e.g., at the AME  116  and at the database proprietor  124 ) to protect privacies of registered users of the database proprietor  124 . 
     An example listing of demographic and behavioral variables from the AME  116  and from the database proprietor  124  is shown in a raw demographic and behavioral variables table  400  of  FIGS.  4 A- 4 C . In the illustrated example, the data interface  102  analyzes the raw demographic and behavioral variables table  400  to select particular ones of the variables to be used for modeling. In addition, the data interface  102  adds variables from the AME  116  corresponding to panelists and recodes the selected ones of the variables from the raw demographic and behavioral variables table  400  of  FIGS.  4 A- 4 C . In the illustrated example, the data interface  102  generates a recoded demographic and behavioral variables table  500  shown in  FIGS.  5 A and  5 B  with names and definitions of the recoded variables. For example, the data interface  102  computes several values to index the degree of demographic match/mismatch between the reference demographic data  112  provided by the AME  116  and the self-reported demographic data  118  provided by the database proprietor  124 . In the illustrated example, the data interface  102  assigns Boolean values to each person represented in the table in an ‘age_match’ field  502  ( FIG.  5 B ) based on whether their ages in both the reference demographics  112  and the self-reported demographics  118  matched. The data interface  102  of the illustrated example also assigns another Boolean value in a ‘gen_match’ field  504  ( FIG.  5 B ) if the genders matched across the reference demographics  112  and the self-reported demographics  118 . The data interface  102  of the illustrated example also assigns a third Boolean value in a ‘perfect_match’ field  506  ( FIG.  5 B ) for the conjunction of matches in both age and gender. 
     At least some of the variables shown in the recoded demographic and behavioral variables table  500  for model generation are recoded from their raw form from the raw demographic and behavioral variables table  400  of  FIGS.  4 A- 4 C  to be better or more meaningfully handled by a recursive partitioning tool (e.g., R Party Package). In the illustrated example of  FIGS.  5 A and  5 B , the data interface  102  recodes continuous variables as quartile and decile categories when the median is greater than zero or otherwise as Booleans. In addition, the data interface  102  places categorical variables coded as integers with ordinally matched letters of the alphabet. 
     In example ideal situations, there will be one-to-one user-level matches for panelists and database proprietor registered users between the recoded cookies records with no duplicates. However, when cookies are collected (e.g., using a household web client meter) from client computer sessions, a single household member may generate more than one cookie and/or multiple household members of the same panel household may be tracked using the same cookie value. As such, cookie records recoded based on the recoded demographic and behavioral variables table  500  may contain duplicates or partial duplicates. In the illustrated example, the data interface  102  processes the recoded cookie records to filter out duplicate panelist and/or self-reported user records merged therein. The data interface  102  of the illustrated example flags each recoded cookie record with a first Boolean flag based on whether a panel member assignment of a cookie from a browsing session that it came from matched a registered user of the database proprietor  124  to whom it was classified. In addition, the data interface  102  flags each recoded cookie record with a second Boolean flag based on whether the panel member assignment of the cookie matches a cookie from a user login prompt of the database proprietor  124 . The data interface  102  then populates a modeling subset in the panelist-user data  126  with recoded cookie records having true values for both Boolean flags as well as any other records with non-duplicated cookie values, provided that they did not introduce mismatched gender data into the model (perfect_match=1). In the illustrated example, the data interface  102  provides the panelist-user data  126  for use by the modeler  104 . 
     In the illustrated example of  FIG.  1   , the apparatus  100  is provided with the modeler  104  to generate a plurality of training models  128 . The apparatus  100  selects from one of the training models  128  to serve as an adjustment model  132  that is deliverable to the database proprietor  124  for use in analyzing and adjusting other self-reported demographic data in the user account database  122  as discussed below in connection with  FIG.  3   . In the illustrated example, each of the training models  128  is generated from a training set selected from the panelist-user data  126 . For example, the modeler  104  generates each of the training models  128  based on a different 80% of the panelist-user data  126 . In this manner, each of the training models  128  is based on a different combination of data in the panelist-user data  126 . 
     Each of the training models  128  of the illustrated example includes two components, namely tree logic and a coefficient matrix. The tree logic refers to all of the conditional inequalities characterized by split nodes between root and terminal nodes, and the coefficient matrix contains values of a probability density function (PDF) of AME demographics (e.g., panelist ages of age categories shown in an AME age category table  600  of  FIG.  6   ) for each terminal node of the tree logic. In a terminal node table  700  of  FIG.  7   , coefficient matrices of terminal nodes are shown in A_PDF through M_PDF columns  708  in the terminal node table  700 . 
     In the illustrated example, the modeler  104  is implemented using a classification tree (ctree) algorithm from the R Party Package, which is a recursive partitioning tool described by Hothorn, Hornik, &amp; Zeileis, 2006. The R Party Package may be advantageously used when a response variable (e.g., an AME age group of an AME age category table  600  of  FIG.  6   ) is categorical, because a ctree of the R Party Package accommodates non-parametric variables. Another example advantage of the R Party Package is that the two-sample tests executed by the R Party Package party algorithm give statistically robust binary splits that are less prone to over-fitting than other classification algorithms (e.g., such as classification algorithms which utilized tree pruning based on cross-validation of complexity parameters, rather than hypothesis testing). The modeler  104  of the illustrated example generates tree models composed of root, split, and/or terminal nodes, representing initial, intermediate, and final classification states, respectively. 
     In the illustrated examples disclosed herein, the modeler  104  initially randomly defines a partition within the modeling dataset of the panelist-user data  126  such that different 80% subsets of the panelist-user data  126  are used to generate the training models. Next, the modeler  104  specifies the variables that are to be considered during model generation for splitting cases in the training models  128 . In the illustrated example, the modeler  104  selects ‘rpt-agecat’ as the response variable for which to predict. As shown in  FIG.  5 B , ‘rpt-agecat’ represents AME reported ages of panelists collapsed into buckets.  FIG.  6    shows an example AME age category table  600  containing a breakdown of age groups established by the AME  116  for its panel members. An example advantage of predicting for groups of ages rather than exact ages is that it is relatively simpler to predict accurately for a bigger target (e.g., a larger quantity of people). 
     In the illustrated example, the modeler  104  uses the following variables as predictors from the self-reported demographics  118  and the behavioral data  120  of the database proprietor  124  to split the cases: Age, gendercat, hsyear_bln (year of high school graduation), current_address_present (current address is present), self_report_zip_bln, asprofilepicturepresent (user profile picture is present), screenname_present (screen name is present), mobilephone_present (mobile telephone number is present), birthdayprivacy (birthday is hidden as private), friend_count_iqr/idr (quantity of friends), dbpage_iqr/idr, active30 day (user activity occurred within last 30 days), active7 day (user activity occurred within last 7 days), mobile_active7 day (user activity occurred within last 7 days via a mobile device), web_active7 day (web browsing user activity occurred within last 7 days), user_cluster, user_assigned_cluster, reg_with_edu_email_bln (registered email address contains a.edu domain), using_edu_email_bln (user has used email address containing a.edu domain), median_friends_age (median age of online friends), median_friends_regage (median age of online registered friends), and percent_female_friends_iqr/idr (percent of friends that are female). These variables are shown in the recoded demographic and behavioral variables table  500  of  FIGS.  5 A and  5 B . In the illustrated example, the modeler  104  omits any variable having little to no variance or a high number of null entries. 
     In the illustrated example, the modeler  104  performs multiple hypothesis tests in each node and implements compensations using standard Bonferroni adjustments of p-values. The modeler  104  of the illustrated example chooses a standard minimum of 0.05 alpha/p criterion for all splits, and at least 25 cases in final terminal nodes. For instances with small quantities of records in the panelist-user data  126 , terminal node classifications with less than 25 cases may exhibit low stability. 
     In the illustrated example, any single training model  128  generated by the modeler  104  may exhibit unacceptable variability in final analysis results procured using the training model  128 . To provide the apparatus  100  with a training model  128  that operates to yield analysis results with acceptable variability (e.g., a stable or accurate model), the modeler  104  of the illustrated example executes a model generation algorithm iteratively (e.g., one hundred (100) times) based on the above-noted parameters specified by the modeler  104 . 
     For each of the training models  128 , the apparatus  100  analyzes the list of variables used by the training model  128  and the distribution of output values to make a final selection of one of the training models  128  for use as the adjustment model  132 . In particular, the apparatus  100  performs its selection by (a) sorting the training models  128  based on their overall match rates collapsed over age buckets (e.g., the age categories shown in the AME age category table  600  of  FIG.  6   ); (b) excluding ones of the training models  128  that produce results beyond a standard deviation from an average of results from all of the training models  128 ; (c) from those training models  128  that remain, determining which combination of variables occurs most frequently; and (d) choosing one of the remaining training models  128  that outputs acceptable results that recommend adjustments to be made within problem age categories (e.g., ones of the age categories of the AME age category table  600  in which ages of the self-reported demographics  118  are false or inaccurate) while recommending no or very little adjustments to non-problematic age categories. In the illustrated example, one of the training models  128  selected to use as the adjustment model  132  includes the following variables: dbp_age (user age reported to database proprietor), dbp_friend_count_iqr/idr (number of online friends), dbp_median_friends_regage (median age of online registered friends), dbp_birthdayprivacy (birthday is hidden as private), dbp_median_friends_age (median age of online friends), dbp_hsyear_bln (year of high school graduation), and dbp_dbpage_iqr (age reported to database proprietor). 
     In the illustrated example, to evaluate the training models  128 , output results  130  are generated by the training models  128 . Each output result set  130  is generated by a respective training model  128  by applying it to the 80% data set of the panelist-user data  126  used to generate it and to the corresponding 20% of the panelist-user data  126  that was not used to generate it. In this manner, the analyzer  106  can perform within-model comparisons based on results from the 80% data set and 20% data set to determine which of the training models  128  provide consistent results across data that is part of the training model (e.g., the 80% data set used to generate the training model) and data to which the training model was not previously exposed (e.g., the 20% data set). In the illustrated example, for each of the training models  128 , the output results  130  include a coefficient matrix (e.g., A_PDF through M_PDF columns  708  of  FIG.  7   ) of the demographic distributions (e.g., age distributions) for the classes (e.g., age categories shown in an AME age category table  600  of  FIG.  6   ) of the terminal nodes. 
       FIG.  7    shows an example terminal node table  700  showing tree model predictions for multiple leaf nodes of the output results  130 . The example terminal node table  700  shows three leaf node records  702   a - c  for three leaf nodes generated using the training models  128 . Although only three leaf node records  702   a - c  are shown in  FIG.  7   , the example terminal node table  700  includes a leaf node record for each AME age falling into the AME age categories or buckets shown in the AME age category table  600 . 
     In the illustrated example, each output result set  130  is generated by running a respective training model  128  to predict the AME age bucket (e.g., the age categories of the AME age category table  600  of  FIG.  6   ) for each leaf. The analyzer uses the resulting predictions to test the accuracy and stability of the different training models  128 . In examples disclosed herein, the training models  128  and the output results  130  are used to determine whether to make adjustments to demographic information (e.g., age), but are not initially used to actually make the adjustments. For each row in the terminal node table  700 , which corresponds to a distinct terminal node (T-NODE) for each training model  128 , the accuracy is defined as the proportion of database proprietor observations that have an exact match in age bucket to the AME age bucket (e.g., a column titled ‘DBP_ACC’ in the terminal node table  700  of  FIG.  7   ). In the illustrated example, the analyzer  106  evaluates each terminal node individually. 
     In the illustrated example, the analyzer  106  evaluates the training models  128  based on two adjustment criteria: (1) an AME-to-DBP age bucket match, and (2) out-of sample-reliability. Prior to evaluation, the analyzer  106  modifies values in the coefficient matrix (e.g., the A_PDF through M_PDF columns  708  of  FIG.  7   ) for each of the training models  128  to generate a modified coefficient matrix (e.g., A-M columns  710  of  FIG.  7   ). By generating the modified coefficient matrix, the analyzer  106  normalizes the total number of users for particular training model  128  to one such that each coefficient in the modified coefficient matrix represents a percentage of the total number of users. In this manner, after the analyzer  106  evaluates the coefficient matrix (e.g., the A_PDF through M_PDF columns  708  of  FIG.  7   ) for each terminal node of the training models  128  against the two adjustment criteria (e.g., (1) an AME-to-DBP age bucket match, and (2) out-of sample-reliability), the analyzer  106  can provide a selected modified coefficient matrix of the A-M columns  710  of  FIG.  7    as part of the adjustment model  132  deliverable for use by the database proprietor  124  on any number of users. 
     During the evaluation process, the analyzer  106  performs AME-to-DBP age bucket comparisons, which is a within-model evaluation, to identify ones of the training models  128  that do not produce acceptable results based on a particular threshold. In this manner, the analyzer  106  can filter out or discard ones of the training models  128  that do not show repeatable results based on their application to different data sets. That is, for each training model  128  applied to respective 80%/20% data sets, the analyzer  106  generates a user-level DBP-to-AME demographic match ratio by comparing quantities of DBP registered users that fall within a particular demographic category (e.g., the age ranges of age categories shown in an AME age category table  600  of  FIG.  6   ) with quantities of AME panelists that fall within the same particular demographic category. For example, if the results  130  for a particular training model  128  indicate that 100 AME panelists fall within the 25-29 age range bucket and indicate that 90 DBP users fall within the same bucket (e.g., an age bucket of age categories shown in an AME age category table  600  of  FIG.  6   ), the user-level DBP-to-AME demographic match ratio for that training model  128  is 0.9 (90/100). If the user-level DBP-to-AME demographic match ratio is below a threshold, the analyzer  106  identifies the corresponding one of the training models  128  as unacceptable for not having acceptable consistency and/or accuracy when run on different data (e.g., the 80% data set and the 20% data set). 
     After discarding unacceptable ones of the training models  128  based on the AME-to-DBP age bucket comparisons of the within-model evaluation, a subset of the training models  128  and corresponding ones of the output results  130  remain. The analyzer  106  then performs an out-of-sample performance evaluation on the remaining training models  128  and the output results  130 . To perform the out-of-sample performance evaluation, the analyzer  106  performs a cross-model comparison based on the behavioral variables in each of the remaining training models  128 . That is, the analyzer  106  selects ones of the training models  128  that include the same behavioral variables. For example, during the modeling process, the modeler  104  may generate some of the training models  128  to include different behavioral variables. Thus, the analyzer  106  performs the cross-model comparison to identify those ones of the training models  128  that operate based on the same behavioral variables. 
     After identifying ones of the training models  128  that (1) have acceptable performance based on the AME-to-DBP age bucket comparisons of the within-model evaluation and (2) include the same behavioral variables, the analyzer  106  selects one of the identified training models  128  for use as the deliverable adjustment model  132 . After selecting one of the identified training models  128 , the adjuster  108  performs adjustments to the modified coefficient matrix of the selected training model  128  based on assessments performed by the analyzer  106 . 
     The adjuster  108  of the illustrated example is configured to make adjustments to age assignments only in cases where there is sufficient confidence that the bias being corrected for is statistically significant. Without such confidence that an uncorrected bias is statistically significant, there is a potential risk of overzealous adjustments that could skew age distributions when applied to a wider registered user population of the database proprietor  124 . To avoid making such overzealous adjustments, the analyzer  106  uses two criteria to determine what action to take (e.g., whether to adjust an age or not to adjust an age) based on a two-stage process: (a) check data accuracy and model stability first, then (b) reassign to another age category only if accuracy will be improved and the model is stable, otherwise leave data unchanged. That is, to determine which demographic categories (e.g., age categories shown in an AME age category table  600  of  FIG.  6   ) to adjust, the analyzer  106  performs the AME-to-DBP age bucket comparisons and identifies categories to adjust based on a threshold. For example, if the AME demographics indicate that there are 30 people within a particular age bucket and less than a desired quantity of DBP users match the age range of the same bucket, the analyzer  106  determines that the value of the demographic category for that age range should be adjusted. Based on such analyses, the analyzer  106  informs the adjuster  108  of which demographic categories to adjust. In the illustrated example, the adjuster  108  then performs a redistribution of values among the demographic categories (e.g., age buckets). The redistribution of the values forms new coefficients of the modified coefficient matrix (e.g., values in the A-M columns  710  of  FIG.  7   ) for use as correction factors when the adjustment model  132  is delivered and used by the database proprietor  124  on other user data (e.g., self-reported demographics  302  and behavioral data  304  of  FIG.  3    corresponding to users for which media impressions are logged). 
     In the illustrated example, the adjuster  108  does not adjust nodes containing data where ‘DBP_ACC’ was already relatively very high. In the illustrated example, DBP_ACC′ stands for database proprietor accuracy, and it is indicative of the amount of accuracy in demographic data (e.g., age) in the self-reported demographic data of the database proprietor  124 . This accuracy measure is a proportion ranging from zero to one, thus, the variance in ‘DBP_ACC’ between different nodes could be characterized by the binomial distribution. In the illustrated example, to determine which nodes to adjust, the adjuster  108  performs calculations at the 99% percent confidence level, with the binomial equation ‘DBP_ACC’+/−2.3*sqrt(′DBP_ACC′*(1−‘DBP_ACC’)/N) where N is the training sample size within the leaf (e.g., a ‘WTS.TRAIN’ column in the terminal node table  700  of  FIG.  7   ). If the upper limit of these calculations exceeds or equals 100% for a leaf (e.g., one of the leaf node records  702   a - c ) then no adjustment (e.g., an adjustment of age) is made for that leaf. In other words, adjustments are not made to data that already appears to be of relatively very high accuracy (e.g., based on the database proprietor accuracy parameter ‘DBP_ACC’). 
     For each row of the terminal node table  700 , the analyzer  106  defines the training and test accuracy as the proportion of observations where AME age bucket matches the predicted age bucket for a respective leaf node. In the illustrated example, the analyzer  106  calculates the error as one minus the accuracy proportion. The analyzer  106  performs this calculation for the training (leaf accuracy (‘LEAFACC’)) and test (out of sample accuracy (‘OOSACC’)). In the illustrated example, the analyzer  106  does not use a separate training model  128  for the test data set (e.g., a 20% portion of the panelist-user data  126 ). Instead, the training model  128  is used to score and predict the test data set. The difference between ‘LEAFACC’ and ‘OOSACC’ should be relatively small if classifications are stable in a node between training and test datasets. Such small difference indicates that the classification is robust enough to be generalized to new observations (e.g., the media impressions-based self-reported demographics  302  and behavioral data  304  of  FIG.  3   ) during, for example, final production use of the adjustment model  132  at the database proprietor  124 . In the illustrated example, the analyzer  106  computes the accuracy of each leaf for the training data set and test data set, then the analyzer  106  computes the differences in these accuracy measures and standardizes them into Z-scores represented in ‘Z’ column  704  of the terminal node table  700  of  FIG.  7   . In the illustrated example, Z-scores have a mean of zero and a standard deviation of one. The analyzer  106  calculates the Z-scores as follows: Z-score per leaf=((Pa−Pt)−Average(Pa−Pt))/standard deviation(Pa−Pt). In the illustrated example, the analyzer  106  determines that the adjuster  108  should not make any adjustments for nodes with Z-scores greater than plus or minus one, because nodes with Z-scores greater than plus or minus one are indicative of performance between training and test data sets that is not stable enough to have sufficient confidence that an adjustment would be only correcting bias and not introducing additional variance. 
     In the illustrated example of  FIG.  7   , each terminal node (e.g., each of the leaf node records  702   a - c ) contains a probability density function (PDF) characterizing the true distribution of AME ages predicted across the age buckets (e.g., the A_PDF through M_PDF columns  708  in the terminal node table  700 ). To determine an age adjustment, the adjuster  108  multiplies each of the age bucket coefficients (e.g., the modified coefficient matrices (MCM) of the A-M columns  710  in the terminal node table  700 ) (which are normalized to sum to one) by the total weights in that tree node (tn) to get the exact number of cases in each AME age bucket using, for example, a convolution process (e.g., Ntn i *MCM). A ‘USEPDF’ column  706  in the terminal node table  700  of  FIG.  7    stores Boolean values representing the outcome of the two-criterion decision process described above. In the ‘USEPDF’ column  706 , zeros indicate high-quality data not to be disturbed whereas ones indicate low accuracy in the self-reported demographics  118  of  FIG.  1    and stable model performance. In the illustrated example of  FIG.  7   , the collection of PDF coefficients for all terminal nodes are noted in the A_PDF through M_PDF columns  708  to form the coefficient matrix. Comparing the coefficient matrices in the A_PDF through M_PDF columns  708  of the terminal node table  700  to modified coefficient matrices of the A-M columns  710 , rows with a ‘USEPDF’ value equal to one have the same values for corresponding coefficients of the coefficient matrices  708  and  710 . Rows with a ‘USEPDF’ value equal to zero have a lone coefficient of one placed into the corresponding database proprietor age bucket being predicted by the leaf node. In such examples, the modified coefficient matrix (MCM) in the A-M columns  710  is part of the adjustment model  132  deliverable from the AME  116  to the database proprietor  124  to inform the database proprietor  124  of inaccuracies in their self-reported demographics  118 . In the illustrated example, multiplying the MCM of the A-M columns  710  by the total counts from the terminal nodes (e.g., the leaf node records  702   a - c ) gives adjusted age assignments. 
     In some examples, to analyze and adjust self-reported demographics data from the database proprietor  124  based on users for which media impressions were logged, the database proprietor  124  delivers aggregate audience and media impression metrics to the AME  116 . These metrics are aggregated not into multi-year age buckets (e.g., such as the age buckets of the AME age category table  600  of  FIG.  6   ), but in individual years. As such, prior to delivering the PDF to the database proprietor  124  for implementing the adjustment model  132  in their system, the adjuster  108  redistributes the probabilities of the PDF from age buckets into individual years of age. In such examples, each registered user of the database proprietor  124  is either assigned their initial self-reported age or adjusted to a corresponding AME age depending on whether their terminal node met an adjustment criteria. Tabulating the final adjusted ages in years, rather than buckets, by terminal nodes and then dividing by the sum in each node splits the age bucket probabilities into a more useable, granular form. 
     In some examples, after the adjuster  108  determines the adjustment model  132 , the model  132  is provided to the database proprietor  124  to analyze and/or adjust other self-reported demographic data of the database proprietor  124 . For example, the database proprietor  124  may use the adjustment model  132  to analyze self-reported demographics of users for which impressions to certain media were logged. In this manner, the database proprietor  124  can generate data indicating which demographic markets were exposed to which types of media and, thus, use this information to sell advertising and/or media content space on web pages served by the database proprietor  124 . In addition, the database proprietor  124  may send their adjusted impression-based demographic information to the AME  116  for use by the AME in assessing impressions for different demographic markets. 
     In the examples disclosed herein, the adjustment model  132  is subsequently used by the database proprietor  124  as shown in  FIG.  3    to analyze other self-reported demographics and behavioral data (e.g., self-reported demographics  302  and behavioral data  304  of  FIG.  3   ) from the user account database  122  to determine whether adjustments to such data should be made. 
       FIG.  3    illustrates an example manner of using the adjustment model  132  of  FIG.  1    to analyze and/or adjust demographic information of audience members. In the illustrated example, the adjustment model  132  is installed at the database proprietor  124  to run in an automated, production basis. In some examples, before providing the adjustment model  132  to the database proprietor  124 , a few adjustments may be made to customize the model  132  to facilitate use by the database proprietor  124 . For example, quartile and decile variables that had been used to generate model fits during evaluation of the training models may be reverted back to their continuous forms. In addition, the user_assigned_cluster variable may be excluded because it is a model predicted value that may be too dynamic to use for classification over any extended period of time. 
     In some examples, the database proprietor  124  applies the adjustment model  132  to a single user at a time following advertisement impressions logged for that user, rather than applying the adjustment model  132  to the total count of individuals in a terminal node at the end of a day (or other measureable duration). Each registered user of the database proprietor  124  is placed in a terminal node (e.g., one of the leaf node records  702   a - c  of  FIG.  7   ) depending on their behavior and demographics and then divided fractionally over years of age as described by the PDF in that terminal node. These probabilistic “parts” of a registered user sum over the course of a day&#39;s impressions before they are aggregated and delivered to the AME  116 . In the illustrated examples, rounding up to the nearest person before reporting has a negligible effect on the final counts. 
     During use of the adjustment model  132  to analyze and/or adjust self-reported demographics of the database proprietor  124 , the model  132  receives media impression-based self-reported demographics  302  and media-impression based behavioral data  304  corresponding to registered users of the database proprietor  124  for which one or more media impressions were logged. In the illustrated example, a media impression is logged for a user upon detecting that a webpage rendered on a client computer of the user presented particular tracked media content (e.g., an advertisement, a video/audio clip, a movie, a television program, a graphic, etc.). In some examples, the database proprietor  124  may log media impressions using cookie-logging techniques disclosed in U.S. provisional patent application No. 61/385,553, filed on Sep. 22, 2010, and U.S. provisional application No. 61/386,543, filed on Sep. 26, 2010, both of which are hereby incorporated herein by reference in their entireties. 
     In the illustrated example, the adjustment model  132  selects demographic data (e.g., self-reported ages) from the media-impression based self-reported demographics  302  to be analyzed (e.g., self-reported user ages). In addition, the adjustment model  132  selects behavioral data from the media-impression based behavioral data  304  corresponding to behavioral variables (e.g., behavioral variables in the recoded demographic and behavioral variables table  500  of  FIGS.  5 A and  5 B ) used in the adjustment model  132 . 
     In the illustrated example, the database proprietor  124  applies the adjustment model  132  on the selected demographic data (e.g., self-reported ages) and the selected behavioral data to determine whether to make adjustments to the selected demographic data. For example, to perform such an analysis, the adjustment model  132  generates a terminal node table similar to the terminal node table  700  of  FIG.  7   . The adjustment model  132  then analyzes the Z-score for each leaf node record (e.g., the Z-score for the ‘Z’ column of each leaf node record  702   a - c  of  FIG.  7   ) and determines that adjustments should be made for nodes with Z-scores greater than plus or minus one. The adjustment model  132  then determines which nodes should be adjusted. 
     In the illustrated example, the adjustment model  132  adjusts demographic data (e.g., self-reported age) of nodes that it identified as needing adjustment. In particular, the adjustment model  132  uses a statistical analysis, such as a Bayesian analysis, to compare the self-reported demographics (e.g., self-reported ages) needing adjustment with a probability distribution of accurate ages grouped into similar behavioral categories as behavioral categories selected for the self-reported demographics. In the illustrated example, the probability distribution of accurate ages grouped into similar behavioral categories are provided in the adjustment model  132  corresponding to panelists for which behaviors indicate similar behavioral categories. For example, the Bayesian analysis may be performed on self-reported ages of users having a certain percentage of friends (e.g., online social networking connections) that graduated high school (hs) within a particular median number of years as they did. In this manner, the adjustment model  132  may use the Bayesian analysis to determine relatively most suitable adjustments to be made for each self-reported age. After the adjustment model  132  adjusts the self-reported demographics, the adjustment model  132  outputs the adjusted results as adjusted general-user demographics data  306 . In some examples the database proprietor  124  can provide the adjusted general-user demographics data  306  for corresponding media impressions to the AME  116 . 
       FIGS.  8 A and  8 B  illustrate a flow diagram representative of example machine readable instructions that may be executed to generate the adjustment model  132 , analyze demographic data (e.g., the media-impression based self-reported demographics  302  of  FIG.  1   ) based on the adjustment model  132 , and/or adjust the demographic data (e.g., the media-impression based self-reported demographics  302 ). The example processes of  FIGS.  8 A and  8 B  may be implemented using machine readable instructions that, when executed, cause a device (e.g., a programmable controller, processor (e.g., the processor  912  of  FIG.  9   ), or other programmable machine or integrated circuit) to perform the operations shown in  FIGS.  8 A and  8 B . For instance, the example processes of  FIGS.  8 A and  8 B  may be performed using a processor, a controller, and/or any other suitable processing device. For example, the example processes of  FIGS.  8 A and  8 B  may be implemented using coded instructions stored on one or more tangible machine readable mediums such as one or more of a flash memory, a read-only memory (ROM), and/or a random-access memory (RAM). 
     As used herein, the term tangible machine readable medium or tangible computer readable medium is expressly defined to include any type of computer readable storage and to exclude propagating signals. Additionally or alternatively, the example processes of  FIGS.  8 A and  8 B  may be implemented using coded instructions (e.g., computer readable instructions) stored on one or more non-transitory computer readable mediums such as one or more of a flash memory, a read-only memory (ROM), a random-access memory (RAM), a cache, or any other storage media in which information is stored for any duration (e.g., for extended time periods, permanently, 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 medium and to exclude propagating signals. 
     Alternatively, the example processes of  FIGS.  8 A and  8 B  may be implemented using any combination(s) of application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), field programmable logic device(s) (FPLD(s)), discrete logic, hardware, firmware, etc. Also, the example processes of  FIGS.  8 A and  8 B  may be implemented as any combination(s) of any of the foregoing techniques, for example, any combination of firmware, software, discrete logic and/or hardware. 
     Although the example processes of  FIGS.  8 A and  8 B  are described with reference to the flow diagram of  FIGS.  8 A and  8 B , other methods of implementing the processes of  FIGS.  8 A and  8 B  may be employed. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, sub-divided, or combined. Additionally, one or more of the example processes of  FIGS.  8 A and  8 B  may be performed sequentially and/or in parallel by, for example, separate processing threads, processors, devices, discrete logic, circuits, etc. 
     Turning to  FIG.  8 A , initially, the data interface  102  ( FIG.  1   ) determines panelists that overlap as also being registered users of a target database proprietor (e.g., the database proprietor  124  of  FIGS.  1  and  2   ) (block  804 ). The data interface  102  retrieves the reference demographics  112  (e.g., from the panel database  114  of the AME  116  of  FIG.  1   ) that correspond to ones of the panelists that are also registered users of the database proprietor  124  (block  806 ). In addition, the data interface  102  retrieves the self-reported demographics  118  and the behavioral data  120  (e.g., from the user account database  122  of the database proprietor  124  of  FIG.  1   ) that correspond to ones of the panelists that are also registered users of the database proprietor  124  (block  808 ). 
     The data interface  102  selects one or more demographic data type(s) and one or more behavioral data type(s) on which to base the training models  128  (block  810 ). For example, the data interface  102  may receive a user-specified demographic data type (e.g., age, gender, etc.) and behavioral data type (e.g., graduation years of high school graduation for friends or online connections, quantity of friends or online connections, quantity of visited web sites, quantity of visited mobile web sites, quantity of educational schooling entries, quantity of family members, days since account creation, ‘.edu’ email account domain usage, percent of friends or online connections that are female, interest in particular categorical topics (e.g., parenting, small business ownership, high-income products, gaming, alcohol (spirits), gambling, sports, retired living, quantity of posted pictures, quantity of received and/or sent messages, etc.). In other examples, the data interface  102  may access a configuration file indicative of the demographic data type and the behavioral data type to use. 
     The data interface  102  selects a portion of the self-reported demographics  118  and the behavioral data  120  corresponding to the demographics data type(s) and behavioral data type(s) selected at block  810 . The data interface  102  generates the panelist-user data  126  ( FIG.  1   ) (block  814 ). In the illustrated examples, the panelist-user data  126  includes demographic data from the reference demographics  112  and the self-reported demographics  118  of  FIG.  1    based on the demographic data type(s) selected at block  810  and includes behavioral data from the behavioral data  120  of  FIG.  1    based on the behavioral data type(s) selected at block  810 . 
     The modeler  104  generates the training models  128  based on different portions (e.g., different 80% subsets) of the panelist-user data  126  (block  816 ). In the illustrated example, the modeler  104  generates one-hundred training models  128  (or more or fewer), and each training model  128  is based on a different 80% of the of the panelist-user data  126 . 
     Each training model  128  is then runs each training model  128  to generate the output results  130  (block  818 ). In the illustrated example, the output results  130  are generated by applying each training model  128  to a respective 80% subset of the panelist-user data  126  used to generate it and the corresponding 20% subset of the panelist-user data  126  that was not used to generate it. The analyzer  106  evaluates the training models  128  based on the output results  130  as discussed above (block  820 ). For example, the analyzer  106  evaluates the training models  128  to identify ones of the training models  128  that (1) have acceptable performance based on the AME-to-DBP age bucket comparisons of the within-model evaluation and (2) include the same behavioral variables. Based on the evaluations of the different training models, the analyzer  106  selects a training model (block  822 ). In the illustrated example, the analyzer  106  selects one of the training models  128  based on it having the least variance (e.g., relatively most stable and accurate). 
     The analyzer  106  determines whether adjustments should be made to any of the demographic categories (block  824 ). In the illustrated example, the demographic categories are the age buckets of the AME age category table  600  of  FIG.  6   , and the analyzer  106  is to determine that adjustments to age assignments only in cases where there is sufficient confidence that the bias being corrected for is statistically significant. Without such confidence that an uncorrected bias is statistically significant, there is a potential risk of overzealous adjustments that could skew age distributions when applied to a wider registered user population of the database proprietor  124 . To avoid making such overzealous adjustments, the analyzer  106  uses two criteria to determine what action to take (e.g., whether to adjust an age or not to adjust an age) based on a two-stage process: (a) check data accuracy and model stability first, then (b) reassign to another age category only if accuracy will be improved and the model is stable, otherwise leave data unchanged. That is, to determine which demographic categories (e.g., age categories shown in an AME age category table  600  of  FIG.  6   ) to adjust, the analyzer  106  performs the AME-to-DBP age bucket comparisons and identifies categories to adjust based on a threshold. For example, if the AME demographics indicate that there are 30 people within a particular age bucket and less than a desired quantity of DBP users match the age range of the same bucket, the analyzer  106  determines that the value of the demographic category for that age range should be adjusted. Based on such analyses, the analyzer  106  can inform the adjuster  108  of which demographic categories to adjust. 
     If the analyzer  106  determines at block  824  that one or more demographic categories should be adjusted, the adjuster  108  adjusts the one or more demographic categories indicated by the analyzer  106  (block  826 ). In the illustrated example, the adjuster  108  performs the adjustment(s) by redistributing values among the demographic categories (e.g., age buckets). The redistribution of the values forms new coefficients of the modified coefficient matrix (e.g., values in the A-M columns  710  of  FIG.  7   ) for use as correction factors when the adjustment model  132  is delivered and used by the database proprietor  124  on other user data (e.g., the media impressions-based self-reported demographics  302  and behavioral data  304  of  FIG.  3   ). After making adjustments at block  826  or if no adjustments are made, the adjustment model  132  is finalized and provided to the database proprietor  124  (block  828 ). 
     After providing the adjustment model  132  to the database proprietor  124 , control advances to block  830  of  FIG.  8 B . In the illustrated example, the operations of  FIG.  8 B  are described with reference to  FIG.  3   . The adjustment model  132  obtains the media impression-based self-reported demographics data  302  and behavioral data  304  of  FIG.  3    (block  830 ) from the user account database  122  of  FIG.  1   . The adjustment model  132  selects a demographic data type and a behavioral data type (block  832 ) for the analysis of the media impression-based self-reported demographics data  302  and behavioral data  304 . The adjustment model  132  organizes the media impression-based self-reported demographics data  302  into buckets (e.g., the AME age groups of the AME age category table  600  of  FIG.  6   ) (block  834 ). 
     The adjustment model  132  is applied to the bucket-organized media impression-based self-reported demographics data  302  and the behavioral data  304  (block  836 ). In the illustrated example, the adjustment model  132  stores the resulting output data of running the adjustment model  132  into a data structure such as the terminal node table  700  of  FIG.  7    for each leaf node of a decision tree associated with the adjustment model  132 . 
     The adjustment model  132  determines whether to adjust demographic data in any bucket (block  838 ). In the illustrated example, the adjustment model  132  determines whether to adjust demographic data by analyzing the data of the decision tree leaf nodes from, for example, the terminal node table  700 . If the adjustment model  132  determines at block  838  that it should adjust demographic data in one or more buckets (e.g., one or more of the AME age groups of the AME age category table  600  of  FIG.  6   ), the adjustment model  132  performs a statistical analysis for the indicated one or more bucket(s) (block  840 ). In the illustrated example, the adjustment model  132  performs the statistical analysis using a Bayesian analysis of the demographic data in the one or more bucket(s) relative to corresponding demographic data in the reference demographics  112  (e.g., demographic data in the reference demographics  112  falling into the same ranges of the one or more buckets to be adjusted). In this manner, the adjustment model  132  can select adjustment amounts based on the Bayesian analysis that will not introduce bias or inaccuracies to other buckets of the demographic data. 
     The adjustment model  132  adjusts the media impression-based self-reported demographic data  302  for the indicated one or more bucket(s) based on the statistical analysis (block  842 ) to generate the adjusted general-user demographics  306 . The database proprietor  124  then determines whether to analyze another demographic data type (block  844 ). For example, the adjustment model  132  may be configured to receive user input on which demographic data types to analyze and/or may be configured to access a configuration file or data structure indicating demographic data types for which to perform adjustment analyses. If the database proprietor  124  determines at block  844  that it should analyze another demographic data type, control returns to block  832 . Otherwise, control advances to block  846 . 
     At block  846 , the database proprietor  124  determines whether to analyze other media impression-based demographic data (block  846 ). For example, the adjustment model  132  may receive other media impression-based demographic data to analyze in addition to the media impression-based demographic data  302 . For example, in some instances, the adjustment model  132  may be configured to continuously process any new media based-impression demographic data and/or to process any new media based-impression demographic data collected within the last 24 hours or other duration. If the database proprietor  124  determines at block  846  that it should analyze other media impression-based demographic data, control returns to block  830 . Otherwise, the example processes of  FIGS.  8 A and  8 B  end. 
       FIG.  9    is an example processor system that can be used to execute the example instructions of  FIGS.  8 A and  8 B  to implement the example apparatus  100  of  FIG.  1   . As shown in  FIG.  9   , the processor system  910  includes a processor  912  that is coupled to an interconnection bus  914 . The processor  912  may be any suitable processor, processing unit, or microprocessor. Although not shown in  FIG.  9   , the system  910  may be a multi-processor system and, thus, may include one or more additional processors that are identical or similar to the processor  912  and that are communicatively coupled to the interconnection bus  914 . 
     The processor  912  of  FIG.  9    is coupled to a chipset  918 , which includes a memory controller  920  and an input/output (I/O) controller  922 . A chipset provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset  918 . The memory controller  920  performs functions that enable the processor  912  (or processors if there are multiple processors) to access a system memory  924 , a mass storage memory  925 , and/or an optical media  927 . 
     In general, the system memory  924  may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. The mass storage memory  925  may include any desired type of mass storage device including hard disk drives, optical drives, tape storage devices, etc. The optical media  927  may include any desired type of optical media such as a digital versatile disc (DVD), a compact disc (CD), or a blu-ray optical disc. 
     The I/O controller  922  performs functions that enable the processor  912  to communicate with peripheral input/output (I/O) devices  926  and  928  and a network interface  930  via an I/O bus  932 . The I/O devices  926  and  928  may be any desired type of I/O device such as, for example, a keyboard, a video display or monitor, a mouse, etc. The network interface  930  may be, for example, an Ethernet device, an asynchronous transfer mode (ATM) device, an 802.11 device, a digital subscriber line (DSL) modem, a cable modem, a cellular modem, etc. that enables the processor system  910  to communicate with another processor system. 
     While the memory controller  920  and the I/O controller  922  are depicted in  FIG.  9    as separate functional blocks within the chipset  918 , the functions performed by these blocks may be integrated within a single semiconductor circuit or may be implemented using two or more separate integrated circuits. 
     Turning to  FIG.  10   , an example communication flow diagram shows an example manner in which an example system  1000  logs ad impressions by clients (e.g., clients  1002 ,  1003 ). The example chain of events shown in  FIG.  10    occurs when a client  1002 ,  1003  accesses a tagged advertisement of the content. Thus, the events of  FIG.  10    begin when a client sends an HTTP request to a server for content, which, in this example, is tagged to forward an exposure request to the ratings entity. In the illustrated example of  FIG.  10   , a web browser  1024  of the client  1002 ,  1003  receives the requested content (e.g., an advertisement  1004 ) from an ad publisher  1006 . It is to be understood that the client  1002 ,  1003  often requests a webpage containing content of interest (e.g., www.weather.com) and the requested webpage contains links to ads that are downloaded and rendered in predefined locations in the webpage. The ads may come from different servers than the requested content. Thus, the requested content may contain instructions that cause the client  1002 ,  1003  to request the ads (e.g., from an ad publisher  1006 ) as part of the process of rendering the webpage originally requested by the client. Either the webpage, the ad, or both may be tagged. In the illustrated example, the uniform resource locator (URL) of the ad publisher is illustratively named http://my.advertiser.com. 
     The advertisement  1004  is tagged with the beacon instructions  1008 . Initially, the beacon instructions  1008  cause the web browser  1024  of the client  1002  or  1003  to send a beacon request  1010  to a panelist monitor system  1012  (e.g., of the AME  116  of  FIG.  1   ) when the ad  1004  is displayed. In the illustrated example, the web browser  1024  sends the beacon request  1010  using an HTTP request addressed to the URL of the panelist monitor system  1012  (e.g., to a server of the panelist monitor system  1012 ). The beacon request  1010  includes one or more of a campaign ID, a creative type ID, and/or a placement ID associated with the advertisement  1004 . In addition, the beacon request  1010  includes a document referrer (e.g., www.acme.com), a timestamp of the ad impression, and a publisher site ID (e.g., the URL http://my.advertiser.com of the ad publisher  1006 ). In addition, if the web browser  1024  of the client  1002  or  1003  contains a panelist monitor cookie, the beacon request  1010  will include the panelist monitor cookie. In other example implementations, the cookie may not be passed until the client  1002  or  1003  receives a request sent by a server of the panelist monitor system  1012  in response to, for example, the panelist monitor system  1012  receiving the beacon request  1010 . 
     In response to receiving the beacon request  1010 , the panelist monitor system  1012  logs an ad impression by recording the ad identification information (and any other relevant identification information) contained in the beacon request  1010 . In the illustrated example, the panelist monitor system  1012  logs the impression regardless of whether the beacon request  1010  indicated a user ID that matched a user ID of a panelist member. However, if the user ID (e.g., the panelist monitor cookie) matches a user ID of a panelist member set by and, thus, stored in the record of the ratings entity subsystem (e.g., the AME  116  of  FIG.  1   ), the logged ad impression will correspond to a panelist of the panelist monitor system  1012 . If the user ID does not correspond to a panelist of the panelist monitor system  1012 , the panelist monitor system  1012  will still benefit from logging an ad impression even though it will not have a user ID record (and, thus, corresponding demographics) for the ad impression reflected in the beacon request  1010 . 
     To compare panelist demographics (e.g., for accuracy or completeness) of the panelist monitor system  1012  with demographics at partner sites and/or to enable a partner site to attempt to identify the client and log the impression, the panelist monitor system  1012  returns a beacon response message  1014  to the web browser  1024  of the client  1002 ,  1003  including an HTTP  302  redirect and a URL of a participating partner. The HTTP  302  redirect instructs the web browser  1024  of the client  1002 ,  1003  to send a second beacon request  1016  to the particular partner (e.g., one of the partners A  1018  or B  1020  which may be the database proprietor  124  of  FIGS.  1  and  3   ). In the illustrated example, the panelist monitor  1012  determines the partner specified in the beacon response  1014  using its rules/ML engine based on, for example, empirical data indicative of which partner should be preferred as being most likely to have demographic data for the user ID. In other examples, the same partner is always identified in the first redirect message and that partner always redirects the client  1002 ,  1003  to the same second partner when the first partner does not log the ad impression. In other words, a set hierarchy of partners is defined and followed such that the partners are “daisy chained” together in the same predetermined order rather than them trying to guess a most likely database proprietor to identify an unknown client  1003 . 
     Prior to sending the beacon response  1014  to the web browser  1024  of the client  1002 ,  1003 , the panelist monitor system  1012  replaces a site ID (e.g., a URL) of the ad publisher  1006  with a modified site ID discernable only by the panelist monitor system  1012  as corresponding to the ad publisher  1006 . In some example implementations, the panelist monitor system  1012  may also replace the host website ID (e.g., www.acme.com) with another modified site ID discernable only by the panelist monitory system  1012  as corresponding to the host website. In this way, the source(s) of the ad and/or the host content are masked from the partners. In the illustrated example, the panelist monitor system  1012  maintains a publisher ID mapping table  1022  that maps original site IDs of ad publishers with modified site IDs created by the panelist monitor system  1012  to obfuscate or hide ad publisher identifiers from partner sites. In addition, the panelist monitor system  1012  encrypts all of the information received in the beacon request  1010  and the modified site ID to prevent any intercepting parties from decoding the information. The panelist monitor system  1012  sends the encrypted information in the beacon response  1014  to the web browser  1024 . In the illustrated example, the panelist monitor system  1012  uses an encryption that can be decrypted by the selected partner site specified in the HTTP  302  redirect. 
     In response to receiving the beacon response  1014 , the web browser  1024  of the client  1002 ,  1003  sends the beacon request  1016  to the specified partner site, which is the partner A  1018  (e.g., the database proprietor  124  of  FIGS.  1  and  3   ) in the illustrated example. The beacon request  1016  includes the encrypted parameters from the beacon response  1014 . The partner A  1018  (e.g., Facebook) decrypts the encrypted parameters and determines whether the client matches a registered user of services offered by the partner A  1018 . This determination involves requesting the client  1002 ,  1003  to pass any cookie it stores that had been set by partner A  1018  and attempting to match the received cookie against the cookies stored in the records of partner A  1018 . If a match is found, partner A  1018  has positively identified a list  1002 ,  1003 . Accordingly, the partner A  1018  site logs an ad impression in association with the demographics information of the identified client. This log (which includes the undetectable source identifier) is subsequently provided to the ratings entity for processing into GRPs as discussed below. In the event partner A  1018  is unable to identify the client  1002 ,  1003  in its records (e.g., no matching cookie), the partner A  1018  does not log an ad impression. 
     In some example implementations, if the user ID does not match a registered user of the partner A  1018 , the partner A  1018  may return a beacon response  1026  including a failure or non-match status or may not respond at all, thereby terminating the process of  FIG.  10   . However, in the illustrated example, if partner A  1018  cannot identify the client  1002 ,  1003 , partner A  1018  returns a second HTTP  302  redirect message  1026  to the client  1002 ,  1003 . For example, if the partner A  1018  site has logic (e.g., similar to the rules/ml engine) to specify another partner (e.g., partner B  1020  or any other partner) likely to have demographics for the user ID, then the beacon response  1026  may include an HTTP  302  redirect along with the URL of the other partner. Alternatively, in the daisy chain approach discussed above, the partner A  1018  site may always redirect to the same next partner (e.g., partner B  1020 ) whenever it cannot identify the client  1002 ,  1003 . When redirecting, the partner A  1018  site of the illustrated example encrypts the ID, timestamp, referrer, etc. parameters using an encryption that can be decoded by the next specified partner. 
     As a further alternative, if the partner A site  1018  does not have logic to select a next best suited partner likely to have demographics for the user ID and is not daisy chained to a next partner, the beacon response  1026  can redirect the client  1002 ,  1003  to the panelist monitor system  1012  with a failure or non-match status. In this manner, the panelist monitor system  1012  can use its rules/ML engine to select a next-best suited partner to which the web browser of the client  1002 ,  1003  should send a beacon request (or, if no such logic is provided, simply select the next partner in a hierarchical (e.g., fixed) list). In the illustrated example, the panelist monitor system  1012  selects the partner B  1020  site, and the web browser  1024  of the client  1002 ,  1003  sends a beacon request to the partner B  1020  site with parameters encrypted in a manner that can be decrypted by the partner B  1020  site. The partner B  1020  site then attempts to identify the client  1002 ,  1003  based on its own internal database. If a cookie obtained from the client  1002 ,  1003  matches a cookie in the records of partner B  1020 , partner B  1020  has positively identified the client  1002 ,  1003  and logs the ad impression in association with the demographics of the client  1002 ,  1003  for later provision to the panelist monitor system  1012 . In the event that partner B  1020  cannot identify the client  1002 ,  1003 , the same process of failure notification or further HTTP  302  redirects may be used by the partner B  1020  to provide a next other partner site an opportunity to identify the client and so on in a similar manner until a partner site identifies the client  1002 ,  1003  and logs the impression, until all partner sites have been exhausted without the client being identified, or until a predetermined number of partner sites failed to identify the client  1002 ,  1003 . 
     Using the process illustrated in  FIG.  10   , ad impressions can be mapped to corresponding demographics even when the ad impressions are not triggered by panel members associated with the audience measurement entity  116  (e.g., ratings entity subsystem). That is, during an ad impression collection or merging process, the panel collection platform  1028  of the ratings entity can collect distributed ad impressions logged by (1) the panelist monitor system  1012  and (2) any particular participating partners (e.g., partners  1018 ,  1020 ). As a result, the collected data covers a larger population with richer demographics information than has heretofore been possible. Consequently, generating accurate, consistent, and meaningful online GRPs is possible by pooling the resources of the distributed databases as described above, the example structures of  FIG.  10    generate online GRPs based on a large combined demographic databases distributed among unrelated parties. The end result appears as if users attributable to the logged ad impressions were part of a large virtual panel formed of registered users of the audience measurement entity because the selection of the participating partner sites can be tracked as if they were members of the audience measurement entities panels. This is accomplished without violating the cookie privacy protocols of the Internet. 
     Periodically or aperiodically, the ad impression data collected by the partners (e.g., partners  1018 ,  1020 ) is provided to the ratings entity (e.g., the AME  116 ) via a panel collection platform  1028 . As discussed above, some user IDs may not match panel members of the panelist monitor system  1012 , but may match registered users of one or more partner sites. During a data collecting and merging process to combine demographic and ad impression data from the ratings entity subsystem (e.g., the AME  116 ) and the partner subsystems (e.g., the database proprietor  124 ), user IDs of some ad impressions logged by one or more partners may match user IDs of ad impressions logged by the panelist monitor system  1012 , while others (most likely many others) will not match. In some example implementations, the ratings entity subsystem (e.g., the AME  116 ) may use the demographics-based ad impressions from matching user ID logs provided by partner sites to assess and/or improve the accuracy of its own demographic data, if necessary. For the demographics-based ad impressions associated with non-matching user ID logs, the ratings entity subsystem (e.g., the AME  116 ) may use the ad impressions to derive demographics-based online GRPs even though such ad impressions are not associated with panelists of the ratings entity subsystem. 
     Turning to  FIG.  11   , the example flow diagram may be performed by the panelist monitor system  1012  ( FIG.  10   ) (e.g., of the AME  116 ) to log demographics-based advertisement impressions and/or redirect beacon requests to web service providers to log demographics-based advertisement impressions. Initially, the panelist monitor system  1012  waits until it has received a beacon request (e.g., the beacon request  1010  of  FIG.  10   ) (block  1102 ). When the panelist monitor system  1012  receives a beacon request (block  1102 ), it determines whether a cookie (e.g., the panelist monitor cookie) was received from the client computer  1002 ,  1003  (block  1104 ). For example, if a panelist monitor cookie was previously set in the client computer  1002 ,  1003 , the beacon request sent by the client computer  1002 ,  1003  to the panelist monitor system  1012  will include the cookie. 
     If the panelist monitor system  1012  determines at block  1104  that it did not receive the cookie in the beacon request (e.g., the cookie was not previously set in the client computer  1002 ,  1003 , the panelist monitor system  1012  sets a cookie (e.g., the panelist monitor cookie) in the client computer  1002 ,  1003  (block  1106 ). For example, the panelist monitor system  1012  may send back a response to the client computer  1002 ,  1003  to ‘set’ a new cookie (e.g., the panelist monitor cookie). 
     After setting the cookie (block  1106 ) or if the panelist monitor system  1012  did receive the cookie in the beacon request (block  1104 ), the panelist monitor system  1012  logs an impression (block  1108 ). As discussed above, the panelist monitor system  1012  logs the impression regardless of whether the beacon request corresponds to a user ID that matches a user ID of a panelist member. However, if the user ID (e.g., the panelist monitor cookie) matches a user ID of a panelist member set by and, thus, stored in the record of the ratings entity subsystem (e.g., the AME  116 ), the logged ad impression will correspond to a panelist of the panelist monitor system  1012 . If the user ID does not correspond to a panelist of the panelist monitor system  1012 , the panelist monitor system  1012  will still benefit from logging an ad impression even though it will not have a user ID record (and, thus, corresponding demographics) for the ad impression reflected in the beacon request  1010 . 
     The panelist monitor system  1012  sends a beacon response (e.g., the beacon response  1014 ) to the client computer  1002 ,  1003  including an HTTP  302  redirect to forward a beacon request (e.g., the beacon request  1016  of  FIG.  10   ) to a next partner (e.g., the partner A  1018  of  FIG.  10   ) and starts a timer (block  1110 ). In the illustrated example, the panelist monitor system  1012  will always send an HTTP  302  redirect at least once to allow at least another partner site to also log an ad impression for the same advertisement (or content). However, in other example implementations, the panelist monitor system  1012  may include rules (e.g., as part of the rules/ML engine) to exclude some beacon requests from being redirected. The timer set at block  1110  is used to wait for a fail status message from the next partner indicating that the next partner did not find a match for the client computer  1002 ,  1003  in its records. 
     If the timeout has not expired (block  1112 ), the panelist monitor system  1012  determines whether it has received a fail status message (block  1114 ). Control remains at blocks  1112  and  1114  until either (1) a timeout has expired, in which case control returns to block  1102  to receive another beacon request or (2) the panelist monitor system  1012  receives a fail status message. 
     If the panelist monitor system  1012  receives a fail status message (block  1114 ), the panelist monitor system  1012  determines whether there is another partner to which a beacon request should be sent (block  1116 ) to provide another opportunity to log an impression. The panelist monitor system  1012  may select a next partner based on a smart selection process using the rules/ML engine or based on a fixed hierarchy of partners. If the panelist monitor system  1012  determines that there is another partner to which a beacon request should be sent, control returns to block  1110 . Otherwise, the example process of  FIG.  11    ends. 
     Although the above discloses example methods, apparatus, systems, and articles of manufacture including, among other components, firmware and/or software executed on hardware, it should be noted that such methods, apparatus, systems, and articles of manufacture are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of these hardware, firmware, and/or software components could be embodied exclusively in hardware, exclusively in firmware, exclusively in software, or in any combination of hardware, firmware, and/or software. Accordingly, while the above describes example methods, apparatus, systems, and articles of manufacture, the examples provided are not the only ways to implement such methods, apparatus, systems, and articles of manufacture. Thus, although certain example methods, apparatus, systems, and articles of manufacture have been described 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.