A support tool for composing questionnaires in social survey data archive SRDQ

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1 20th Workshop on Methodologies and Tools for Complex System Modeling and Integrated Policy Assessment (CSM2006) : Laxenburg, Austria A support tool for composing questionnaires in social survey data archive SRDQ Graduate School of Information Science and Technology, Osaka University, Japan Norihisa Komoda, Shingo Tamura, Yoshitomo Ikkai, Koichi Higuchi

2 1.Social Survey Data Archive The social survey data archive is an archive that collects, storages and disseminates lots of social survey data such as Social Network Survey. Each survey data contains various types of items such as question items, dataset (answers of respondents), sample design, and papers/repots about the survey. Objectives Maintaining the quality of social surveys When composing questionnaires for new surveys, it is imperative to review question items and dataset of existing surveys for maintaining the quality. Effective use of existing data It reduces the need to conduct repetitious surveys for similar purposes. Thus large amounts of survey costs can be eliminated. Education The archive makes it possible to develop social survey methodology lessons using high quality survey data.

3 1.Social Survey Data Archive: SRDQ SRDQ : the Social Research Database on Questionnaires One of the most advanced social survey data archive in Japan. ( developed by Graduate School of Human Science, Osaka University in 2003.

4 1.Social Survey Data Archive: SRDQ SRDQ : the Social Research Database on Questionnaires Specifications Hierarchical textual data Searching system (string search) Dataset analysis system (crosstab, etc.) Subjects, sample designs, papers & reports of each survey are also stored 119 surveys 17,232 items Information Class Information Society Survey Subjects: Information, Social Psychology, etc. Question Items, Dataset, Papers & Reports. Social Network Survey Subjects: Information, Family, etc. Question Items, Dataset, Papers & Reports. Question item 1 Qeestion item 2 Simple string search of question items or surveys

5 1.Social Survey Data Archive: SRDQ SRDQ allows the direct analysis of dataset over the web pages Example: Crosstab analysis In this window, the row and column items of the crosstab are selected. Crosstab analysis alternatives of row item Alternatives of column item A B male female male answered A.

6 Finally, An analyst the analyst selects pushes the variables crosstabs he/she for wants starting to use, analysis. then push >>. 1.Social Survey Data Archive: SRDQ How to execute Crosstab analysis by using SRDQ

7 47% of male use PC, while only 29% of female do. 1.Social Survey Data Archive: SRDQ The Result: Row: Gender Column: PC

8 2. Purpose of the Study To make SRDQ more useful, we planed to add a new function to help researchers in composing new questionnaires. Procedures to compose a new questionnaire: Decide the purpose and the design 3 man-month Intermittent discussion by research group members (approx. 10 researchers), Continue 3 12 months. Summarize existing question items 0.5 man-month Select exiting surveys or question items to compare with new ones 0.75 man-month Create new question items 0.75 man-month Decide the order of question items 0.25 man-month Search for related surveys A tool to support this process has been developed. Survey containing questions

9 2. Purpose of the Study Procedures to compose a new questionnaire: Decide the purpose and the design Summarize existing question items In this process, Summary of Question Items is used. Select exiting surveys or question items to compare with new ones Create new question items A tool to support this process has been developed. Decide the order of question items

10 2. Summary of Question Items Summary of Question Items is a synopsis of similar question items included in particular surveys. Information Society Survey 2001 Information Society Survey 2002 q1. Do you use the q3.do you use on following items? your cell phone or PC a. 1. yes 2. no b. Fax q45.do you use Home Page on your cell phone or PC f.home Page 1. yes 2. no Question Items Do you use the following items? Do you use the following items? Fax Do you use the following items? Home Page ISS 2001 q1a q1b q1f ISS 2002 q3 q45 JGSS 2003 q22a q22b surveys Break down the question items to the minimum units (red underlined). And summarize the similar items/units. Trends of the question items and differences between the surveys become clear. SRDQ Searched with keywords and name of surveys

11 2.Support System for the Summarization Summary of Question Items Surveys Question Items Do you use the following items? Do you use the following items? Fax Do you use the following items? Home Page ISS 2001 q1a q1b q1f ISS 2002 JGSS 2003 It takes approx. 1 week to process only 3 or 4 surveys manually. q3 q45 q22a q22b Goal The automatic creation of the summary that is sufficiently accurate to meet the demands of social survey specialists. And, the provision of the editing interface to correct the errors and to produce a final, completed summary in less time. Evaluation of accuracy: E = W * Non- items + Miss items ( W > 1 ) Number of rows includes detection errors should be under 10%

12 3.Overview of the System Input Surveys + Keywords (ex. Survey A, B, C, D + mail ) Search Target (several surveys) A B C D For similarity judgments of question items, Jaccard Coefficient is used. Output 19 question items about mail 1. How often do you use for each of the purposes listed below? 1.1 business communication a. every day b. 3 or more days a week 1.2 Personal communication with friends a. every day b. 3 or more days a week 1.3 Personal communication with family a. every day b. 3 or more days in a week A B C D q2 q2 q2 q3 q3 q15 q23

13 3. Similarity Judgments by Jaccard Coefficient Original Method Jaccard Coefficient: J = a / (a+b+c) a: number of common words between 2 question items b, c: number of words which appear in only 1 question items Similarity Judgment Calculate similarity for all combination of question items in target surveys The pair which has maximum similarity value will be judged as similar. (Repeat this step while similarity values are higher than the threshold) Q.A1 Q.B1 Q.C1 Q.A1,Q.B1 Q.C1 Q.A2 Q.B2 Q.C2 Q.A2 Q.B2 Q.A1 Q.B1: Maximum similarity Q.C2

14 3.Difficulty of Similarity Judgments 1. Partial match in juxtaposed words Survey A Survey B 1. Treat juxtaposed words as a group 2. Almost all words are same except one core word, but the intended purposes of the questions are different. Survey A Survey B How often do you do the things on this list? Practice flower arranging, tea ceremony, or calligraphy Do you practice cooking, sewing, or calligraphy? How often do you use for personal communication with friends? How often do you use for personal communication with family? 3. Different expression, but asking the same thing. Survey A Survey B 2. Apply a penalty if core words don t match Do you perform following actions in your everyday life? Reuse bathwater for laundering to conserve water. Do you try to do things in this list? Saving resources such as water. Non- Miss Non 3.Apply neighborhood bonus for word matches

15 3.Similarity Judgments (1/2) New similarity measure which uses structural characteristics of surveys 1. Treat juxtaposed words as a group Juxtaposed words can be viewed as a group If one or more words matches in juxtaposed words, treat those words as a group and ignore unmatched words when calculating similarity 2. Apply penalty if core words don t match For pairs of similar question items within one survey, if only a few words differs, that words are recognized as core words. Detect core words before calculating similarity, and decrease similarity value if core words don t match. Survey A Q1-a. How often do you use for each of the purposes? communication with family Q1-a. How often do you use for each of the purposes? business communication core words Survey B Q1-a. How often do you use for each of the purposes? communication with friends Don t Match Under specific conditions, values of existing Jaccard coefficient are adjusted. If a pair within one survey has similarity value higher than 0.6, un-matched words are recognized as core words. Penalty

16 3.Similarity Judgments (2/2) 3.Apply neighborhood bonus for word matches There is significance to the order of the question items. Question items having the same meaning tend to be arranged in the same order. Increase similarity values if highly similar pairs are found in the neighborhood Survey A Q7. Do you perform following actions in your daily life? 1.Turn off lights not in use 2. Reuse bathwater for laundering to conserve water. Question items in the same hierarchical positions High similarity value Survey B Q2. Do you try to do things in this list? 1. Always turn off lights not in use. a. yes b. no 2. Saving resources such as water. a. yes b. no Survey C High similarity value Survey D 1. Do you use on your pc? 1. Do you use on your cell phone or pc? 2. How often do you use ? 2-a. Gathering info for daily life Bonus 2. How many times do you send/receive s? 2-1. To get info about everyday life

17 4.Evaluation of Similarity Judgments (1/2) Compare correct result manually prepared with result using proposed measure and result using Jaccard coefficient only 36 question items about environmental protection (from 3 surveys) Threshold value of similarity judgments T = 0.6 Errors Miss Jaccard Penalty: 0.5, Neighborhood bonus : 0.3 E Rows Rows contain errors 30 5 Proposed T = 0.5 Errors Non- Non- Miss Jaccard E Rows Rows contain errors 28 5 Proposed Non-detections are more problematic than miss detections Evaluation: E = W (3 = number of surveys) * Non- + Miss Non-detection: a pair was judged as not similar while it should be judged as similar Miss detection: a pair was judged as similar while it should be judged as not similar

18 4.Evaluation of Similarity Judgments (2/2) 113 question items about Leisure (from 10 surveys) Penalty: 0.5, Neighborhood bonus : 0.3 Threshold value of similarity judgments T = 0.6 Errors Miss Jaccard E Rows Rows contain errors Proposed T = 0.5 Errors Non- Non- Miss Jaccard E Rows Rows contain errors Proposed Evaluation: E = W (10 = number of surveys) * Non- + Miss Non-detection: a pair was judged as not similar while it should be judged as similar Miss detection: a pair was judged as similar while it should be judged as not similar Non-detection & miss detection are reduced, and thus E is improved Number of rows containing detection errors is under 10% The efficiency of the proposed method has been confirmed.

19 5.Editing Interface The prototype tool has been developed. The editing interface is build as CGI script.(perl). scrolling total 10 surveys scrolling Click to open an editing window Select item to move Specify the destination Possible miss detection: exceeds the threshold but the value is close to the threshold value 0.5~0.6 Possible non-detection: does not exceed the threshold but the value is close to the threshold 0.4~0.5

20 5. Editing Interface Moved an item to a new row (the last row) to correct a detection error. scrolling Survey E total 10 surveys scrolling Possible miss detection moved Possible non-detection

21 5.Evaluation Test of the Editing Interface Evaluation test: compare the time taken to create the summary by hand with the time using the proposed system / interface. Material: 113 question items about Leisure ( from 10 surveys ) Contains 1 non-detection and 9 miss detection ( T = 0.5 ). Manual Proposed System Time taken to create a correct summary 3 hours 20 minutes view & check the question items 15 min. move the items to correct errors 5 min. Possible miss detection Possible non-detection 3 rows contain detection errors 10 question items are moved Possible miss detection: 6 items Possible non-detection: 22 items (All detection errors are displayed as these possible error )

22 6. Conclusions Using structural characteristics of social survey questionnaires, we have developed a support tool for generation of the summary of question items. The proposed method is capable of automatically creating the summary that is sufficiently accuracy to meet the demands of specialists. With the man-machine interface system, final and completed summaries can be generated in less time than manual means.

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