Abstract:
Card-sorting exercises are used to understand how users would intuitively group or sort information topics, in order to better design an instrument that provides these topics, such as a website. When a user is not familiar with some of the topics, they are allowed to leave these items unsorted, in order that wild guesses do not skew the results. The algorithm verifies that the unsorted items are unfamiliar, then tracks instances of unsorted items so that these responses are mathematically removed from the calculations.

Description:
BACKGROUND OF THE INVENTION 
     1. Technical Field 
     The present invention relates generally to a method of gathering and correlating user input regarding relationships between topics, this information being useful in designing web sites, program interfaces, and many other information design applications. Specifically, the present invention provides a method and algorithm for handling a lack of user input in portions of the data gathering process where the user is unfamiliar with some of the items presented. 
     2. Description of Related Art 
     Card sorting is a technique used by the builders of web sites to organize the information on the site and to decide how to label the categories for ease of use. The technique works by gathering data from a number of users regarding their perception of relationships between topics. The strength of the perceived relationships can then drive the design of the site. 
     In a manual version of card sorting, a user is given a set of index cards containing likely topics for the site, one topic per card. The user then sorts the cards into groups according to his perception of which topics belong together. Note that in this exercise, there is no right or wrong way to sort items. This is a subjective exercise that seeks to discover perceptions. Therefore, different users will have a tendency to group items differently, especially as the ideas they represent become the more complex. 
     The input from a number of users can then be correlated in a matrix according to how closely users group each set of two cards together, a methodology known as cluster analysis. Manual correlation and analysis, however, can be tedious. 
     EZSort is a software package created by IBM, Inc., which handles the card sorting process and analysis. EZSort has two parts—USort and EZCalc. USort handles the card sorting exercise for all participants; EZCalc performs cluster analyses on the accumulated data and generates tree diagrams that represent the hierarchical relationships. 
       FIG. 1A  shows a computer screen containing a typical card-sorting exercise handled by USort. In this figure, the “cards” to be sorted are presented on the left side of the screen (the source); the right side of the screen (the target) is where a user sorts the cards into groups separated by horizontal lines, using drag and drop operations. Notice that the cards to be sorted include a wide variety of topics including hardware, software, languages, operating systems, interfaces between users, interfaces between computers, etc. A user&#39;s background and experience will tend to affect the way that he would perceive items as belonging together. 
     When the user is satisfied with the groupings, clicking on the right arrow ( 110 ) causes the program to move to the next step, seen in  FIG. 1B . On this second screen, the user is allowed to designate further, higher-level groupings, if these are deemed desirable. The previously formed groups are presented. The groups can be rearranged, and larger groupings formed by making the lines between high-level groups into double lines. In a third step, which is not shown, the user is allowed to name the categories into which he has grouped items. Once the exercise is complete, the users information is saved to a file for later processing. 
     When all card-sorting exercises have been done, the data goes to EZCalc for analysis. A raw score matrix is created for each participant, according to the following. If two items are not grouped together by the participant, a value of 0 is assigned. If the two items are grouped together in a high-level grouping, but not in the low-level grouping, a value of 1 is assigned. If the two items are grouped together in both the high-level and low-level groupings, a value of 2 is assigned. Thus, each possible pairing of items receives a score of 0, 1, or 2. Next, the raw scores for each pair of items are summed together for all of the participants, forming a total raw score matrix. The values in this matrix are normalized into a similarity matrix by dividing each score by 2·n, where n is the number of participants. Each element in the similarity matrix now has a score of 0 to 1. Items in the similarity matrix are converted into a distance matrix, using the formula
 
 D ( x,y )=1 −S ( x,y )
 
where D(x,y) is an element in the distance matrix for card pair x and y, and
         S(x,y) is a corresponding element in the similarity matrix.       

     Finally, cluster analysis converts the distance matrix into tree diagrams for analysis. 
     While this type of program has been very helpful in speeding up the analysis of card-sorting applications, a problem exists when participants are not familiar with the content of every card. This can happen, for example, when a company provides a variety of specialized, technical products, such as those shown in  FIGS. 1A and 1B . A person who regularly utilizes some of the products may have little or no knowledge in other products. This type of program has previously required each participant to group every card that was presented to them, regardless of their knowledge of the content of the card. By forcing the sorting of “unknown” cards, the relationships involving them are skewed. It would be desirable to have a program that did not force such a choice, but that could deal with this lack of input in some areas. 
     SUMMARY OF THE INVENTION 
     The present invention provides a method and computer algorithm for handling cards that are not sorted by one or more participants and for weighting relational distances accordingly. When a participant does not sort one or more cards, a screen prompt checks to be sure that this is intentional. Then, a record is kept of that card, as well as of the groups the participant formed. When the matrix of responses is created for this participant, any pair that contains an un-selected card is assigned a score of 0. This will be added to the other raw scores to form the summed raw score. At the same time, an unknown matrix is generated for the participant. This matrix is initially all zeros. Whenever a participant does not sort one or both of the cards in a pair, the value for that pair is set to one. A total unknown matrix contains a summary of all unknowns for all participants. When the summed raw scores are normalized, rather than dividing by 2·n, each individual element is divided by 2·(n−U(x,y)), where n is the total number of participants in the card-sorting activity and U(x,y) is the number of participants who did not group at least one of the items in the pair containing x and y. The rest of the calculations remain the same. A final change is made in the display portion of the program. When the tree structure is displayed, any item which some participants did not sort will have a fraction shown next to it, giving the number of participants out of the total who sorted that item. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings, wherein: 
         FIGS. 1A and 1B  show exemplary screens during a the execution of first and second steps of EZSort. 
         FIG. 2  shows a personal computer. 
         FIG. 3  shows a block diagram of a computer system in which the disclosed invention can be used. 
         FIGS. 4A and 4B  is a flowchart representation of a process of card sorting according to a preferred embodiment of the present invention. 
         FIGS. 5A-D  are exemplary screens for a first user as he works through the sorting process. 
         FIGS. 6A and 6B  are exemplary raw score matrices for an individual participant and for all participants respectively; 
         FIGS. 7A and 7B  are exemplary unknown matrices for an individual participant and for all participants respectively; 
         FIG. 8  is an exemplary similarity matrix. 
         FIG. 9  is an exemplary distance matrix; 
         FIG. 10  is an exemplary tree structure derived from the distance matrix of  FIG. 9 . 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     With reference now to the figures and in particular with reference to  FIG. 2 , a pictorial representation of a data processing system in which the present invention may be implemented is depicted in accordance with a preferred embodiment of the present invention. A computer  200  is depicted which includes system unit  202 , video display terminal  204 , keyboard  206 , storage devices  208 , which may include floppy drives and other types of permanent and removable storage media, and mouse  210 . Additional input devices may be included with personal computer  200 , such as, for example, a joystick, touchpad, touch screen, trackball, microphone, and the like. Computer  200  can be implemented using any suitable computer, such as an IBM eServer computer or IntelliStation computer, which are products of International Business Machines Corporation, located in Armonk, N.Y. Although the depicted representation shows a computer, other embodiments of the present invention may be implemented in other types of data processing systems, such as a network computer. Computer  200  also preferably includes a graphical user interface (GUI) that may be implemented by means of systems software residing in computer readable media in operation within computer  200 . 
     With reference now to  FIG. 3 , a block diagram of a data processing system is shown in which the present invention may be implemented. Data processing system  300  is an example of a computer, such as computer  200  in  FIG. 2 , in which code or instructions implementing the processes of the present invention may be located. Data processing system  300  employs a peripheral component interconnect (PCI) local bus architecture. Although the depicted example employs a PCI bus, other bus architectures such as Accelerated Graphics Port (AGP) and Industry Standard Architecture (ISA) may be used. Processor  302  and main memory  304  are connected to PCI local bus  306  through PCI bridge  308 . PCI bridge  308  also may include an integrated memory controller and cache memory for processor  302 . Additional connections to PCI local bus  306  may be made through direct component interconnection or through add-in boards. In the depicted example, local area network (LAN) adapter  310 , small computer system interface SCSI host bus adapter  312 , and expansion bus interface  314  are connected to PCI local bus  306  by direct component connection. In contrast, audio adapter  316 , graphics adapter  318 , and audio/video adapter  319  are connected to PCI local bus  306  by add-in boards inserted into expansion slots. Expansion bus interface  314  provides a connection for a keyboard and mouse adapter  320 , modem  322 , and additional memory  324 . SCSI host bus adapter  312  provides a connection for hard disk drive  326 , tape drive  328 , and CD-ROM drive  330 . Typical PCI local bus implementations will support three or four PCI expansion slots or add-in connectors. 
     An operating system runs on processor  302  and is used to coordinate and provide control of various components within data processing system  300  in  FIG. 3 . The operating system may be a commercially available operating system such as Windows XP, which is available from Microsoft Corporation. An object oriented programming system such as Java may run in conjunction with the operating system and provides calls to the operating system from Java programs or applications executing on data processing system  300 . “Java” is a trademark of Sun Microsystems, Inc. Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as hard disk drive  326 , and may be loaded into main memory  304  for execution by processor  302 . 
     Those of ordinary skill in the art will appreciate that the hardware in  FIG. 3  may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash read-only memory (ROM), equivalent nonvolatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in  FIG. 3 . Also, the processes of the present invention may be applied to a multiprocessor data processing system. 
     For example, data processing system  300 , if optionally configured as a network computer, may not include SCSI host bus adapter  312 , hard disk drive  326 , tape drive  328 , and CD-ROM  330 . In that case, the computer, to be properly called a client computer, includes some type of network communication interface, such as LAN adapter  310 , modem  322 , or the like. As another example, data processing system  300  may be a stand-alone system configured to be bootable without relying on some type of network communication interface, whether or not data processing system  300  comprises some type of network communication interface. As a further example, data processing system  300  may be a personal digital assistant (PDA), which is configured with ROM and/or flash ROM to provide non-volatile memory for storing operating system files and/or user-generated data. 
     The depicted example in  FIG. 3  and above-described examples are not meant to imply architectural limitations. For example, data processing system  300  also may be a notebook computer or hand held computer in addition to taking the form of a PDA. Data processing system  300  also may be a kiosk or a Web appliance. The processes of the present invention are performed by processor  302  using computer implemented instructions, which may be located in a memory such as, for example, main memory  304 , memory  324 , or in one or more peripheral devices  326 - 330 . 
     We will now walk through an embodiment of the innovative process in order to explain it more fully, starting with reference to  FIGS. 4A  and B, which show a flowchart of the process and to  FIGS. 5-9 , which show choices faced by the user and the matrices created by the program as it works. The process begins in the same manner as its predecessor—with the presentation of a screen that offers items for the participant to sort, shown in  FIG. 5A . For the sake of simplicity, we will discuss only eleven cards in this example, although these may be part of a larger study that includes more cards. The eleven cards of interest are shown on the source side of the screen and have arbitrarily been labeled A-K for reference. The first user performs his sorting (step  405 ), as shown in  FIG. 5B . This participant has left items G and K unsorted, as he is unfamiliar with these items. He clicks the arrow to indicate that he is through sorting (step  410 ). The program checks to see if any items remain in the Source field (step  415 ); if not, it skips ahead to the next part of the algorithm (step  430 ); otherwise, the program visually marks (step  420 ) the remaining items in the Source field and presents (step  425 ) the screen seen in  FIG. 5C . This screen notes that items were left in the source side of the screen and seeks to discover if this was intentional. If it was inadvertent, the user clicks on the “no” button and is given another chance to finish sorting (step  415 ); if the user left items because he was not familiar with them, the user clicks on the “yes” button and the program proceeds. At the same time, the program saves a copy of items that were left on the source side as “unknown”. In the next part of the input, the previous groups are presented to allow further, higher level groupings, if desired. In  FIG. 5D , the participant has made further entries. Note that this user has further grouped only two groups (step  430 ), the group containing B and I and the group containing only D. These two groups are separated only by a single line, showing that they are grouped together at a higher level, but not at a lower level. All other groups are separated by double lines. The input phase concludes with part  3 , not specifically shown, in which the user names the higher level groups that he has created (step  435 ). Data will be gathered from a number of participants, each following the process outlined above. Once the data is collected, it is analyzed. 
     The flow for analysis of the data is shown in  FIG. 4B . First, a raw score matrix is formed for each participant (step  450 ). The raw score matrix for the first user is shown in  FIG. 6A . Comparing this matrix to the groupings seen in  FIGS. 5B and 5C , we can note that items B and I were grouped together at both levels and the matrix M(B,I)=2. Likewise, the matrix entries for M(C,H), M(C,J), M(H,J), and M(E,F) are equal to 2. Items in the group containing B and I were grouped with items in the group containing D at the higher levels, although not at the lower levels. Therefore M(B,D) and M(I,D) have values of 1. All other values are zero. Note particularly that there are zeros for any pair which contains items G or K, which were not sorted at all. 
     Additionally, an unknown matrix is created (step  455 ) for the first user, shown in  FIG. 7A . In this unknown matrix, there is a value of 1 for those pairs in which one or both of the items were not sorted; all other values are 0. Thus, any pair containing items G or K is 1. Notably, although there were only two items not sorted, there are nineteen pairs that are affected. This provides some indication of how much a mistaken grouping, done because the user wasn&#39;t familiar with an item, can affect an analysis. 
     In the next step, a total raw score is created for all participants by adding all the values for corresponding matrix positions for all participants (step  460 ). For our hypothetical example, twenty participants completed the sorting exercise, with the total raw score shown in  FIG. 6B . Of these participants, including the first participant, two persons did not sort item G, one did not sort item E, one did not sort item H and one did not sort item K. A total unknown matrix is formed by adding all the corresponding values from the unknown matrices for all participants (step  465 ).  FIG. 7B  is the total unknown matrix. This matrix shows how many persons did not address a particular pair. 
     Next, each of the raw scores is normalized to a value representative of the similarity of the items as seen by the participants. This similarity matrix, shown in  FIG. 8 , is formed by dividing each total raw score by the highest score possible for that pair (step  470 ). Since the number of persons NOT answering each question is shown in the total unknown matrix, the highest score possible for pair x,y is 2·(20−N(x,y)), where N(x,y) is the corresponding entry in the total unknown matrix and 20 is the number of participants. To generalize,
 
 S ( x,y )= R ( x,y )/(2·( n−N ( x,y )))
 
where S(x,y) is an entry in the similarity matrix for pair x,y;
 
     R(x,y) is a corresponding entry in the total raw score matrix, and 
     n is the number of participants in the study. 
     Once the similarity matrix has been created, it is transformed into the distance matrix (step  480 ) by subtracting each similarity entry from 1 to create the corresponding distance entry. That is
 
 D ( x,y )=1 −S ( x,y )
 
Where S(x,y) is an entry in the similarity matrix for pair x and y and D(x,y) is the corresponding entry in the distance matrix. The distance matrix is shown in  FIG. 9 .
 
     The distance matrix is used to create the tree structure that is output by the program.  FIG. 10  shows that portion of the tree that includes the items in our example. 
     As shown by this example, programs such as EZSort are now able to provide more appropriate relationship information, due to elimination of the distortion produced when a user does not understand an entry. 
     It is important to note that while the present invention has been described in the context of a method run on a computer, those of ordinary skill in the art will appreciate that the processes of the present invention are capable of being distributed in the form of a computer readable medium of instructions and a variety of forms and that the present invention applies equally regardless of the particular type of signal bearing media actually used to carry out the distribution. Examples of computer readable media include recordable-type media, such as a floppy disk, a hard disk drive, a RAM, CD-ROMs, DVD-ROMs, and transmission-type media, such as digital and analog communications links, wired or wireless communications links using transmission forms, such as, for example, radio frequency and light wave transmissions. The computer readable media may take the form of coded formats that are decoded for actual use in a particular data processing system. 
     The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention, the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 
     For example, the specific algorithm used here to measure a logical “distance” between items is based on an answer having a scale of 0 to 1. However, any reasonable scale could used, as long as it arrives at relative distances apart and a different algorithm could be used if it made allowances for removing items not sorted.