1. Common and Specific Approaches in the Analysis of Q-Sort Data with PQMethod Peter Schmolck Universität der Bundeswehr München by OfficeOne: AutoDateTime 9:24 (0:02:59)

2. 2 Outline Outline 1.H ISTORY 2.T YPICAL S TEPS OF A NALYSIS J OB ’ S T RAVEL -S TUDY E XAMPLE 2.1Creating the Study Files, Entering the Data 2.2Analysis: 1st Overview - Likely Number of Factors 2.3Analysis: Choice of Solution - Fine Tuning 2.4Results and Interpretation 9:25 (0:03:52)20:01

3. 3 Outlinecontd. 3.MORE SPECIFIC QUESTIONS AND SOLUTIONS WITH PQMETHOD 3.1Relating Q Factors to External Data (with Travel-Study Example) 3.2Splitting up a Bipolar Factor into its Two Poles (Mother-in-Law Study by Andrea Kettenbach) 9:25 (0:04:07)30:03

4. 4 1. History 1. History Intro 9:24 (0:02:52)0:05

5. 5 20 May 1992 20 May 1992 Steven Brown announced availability of QMETHOD - Author John Atkinson - Only for mainframe computer systems (IBM, later also VAX) - Mainframe era over at that time, replaced by personal computers 1 March 1992 1 March 1992 Windows 3.1 1. History 10:38 (0:00:10)0:05

6. 6 10 Aug 1994 10 Aug 1994 Peter Schmolck announced PC Version of Atkinson’s QMETHOD - downloadable from WWW homepage 09 Aug 1996 09 Aug 1996 “QMethod Page” “http://www.rz.unibw-muenchen.de/~p41bsmk/qmethod” - still OK (but also, e.g. schmolck.org/qmethod) 1. History contd. 9:28 (0:06:52)0:06

7. 7 20 May 1997 20 May 1997 Version 2 beta Selection of features and improvements added - automatic pre-flagging (greatly facilitated program debugging!) - “.dat” file format, with rows=sorts (mainframe “.raw” still supported) - Sort-Ids - Consensus-statements table, eventually resolved the non-distinguishing vs. consensus-statements riddle - Principal Components extraction 28 Nov 2002 28 Nov 2002 The current release, 2.11 1. Historycontd. F1 F2 F3 0 +1 +4 distinquishing for F3 0 +1 +3 not distinguishing for any F, but not consensus (diff. F1 – F3!) 0 +1 +2 not distinguishing for any F, and consensus 9:34 (0:13:17)0:07

8. 8 2. Typical Steps of Analysis Job‘s Travel-Study Example 2. Typical Steps of Analysis Job‘s Travel-Study Example 2.1 Creating the Study Files, Entering the Data 2.2 Analysis: 1st Overview - Likely Number of Factors 2.3 Analysis: Choice of Solution - Fine Tuning 2.4 Results and Interpretation 2. Typical Steps of AnalysisOutline 9:35 (0:13:50)0:10*

9. 9 Job‘s OS Publication on which the following demonstrations are based 2. Typical Steps.. Travel Study Example 9:35 (0:14:25)0:11

10. 10 C:\PQMETHOD\projects\q-conference>pqmethod travel +---------------------------------------------------+ | PQMethod - 2.11 | | (November 2002) | +---------------------------------------------------| | by Peter.Schmolck@unibw-muenchen.de | | Adapted from Mainframe-Program QMethod | | by John Atkinson at KSU | +---------------------------------------------------| | The QMethod Page: | | http://www.rz.unibw-muenchen.de/~p41bsmk/qmethod/ | +---------------------------------------------------+ Hit ENTER to begin 2.1 Creating the Study FilesIntro 9:35 (0:14:35)0:11

11. 11 Current Project is... C:\PQMETHOD\projects\q-conference/travel Choose the number of the routine you want to run and enter it. 1 - STATES - Enter (or edit) the file of statements 2 - QENTER - Enter q sorts (new or continued) 3 - QCENT - Perform a Centroid factor analysis 4 - QPCA - Perform a Principal Components factor analysis 5 - QROTATE - Perform a manual rotation of the factors 6 - QVARIMAX - Perform a varimax rotation of the factors 7 - QANALYZE - Perform the final Q analysis of the rotated factors 8 - View project files travel.* X - Exit from PQMethod Last Routine Run Successfully - (Initial) 2 Checking old input data file.... Enter the title of your study to a max of 68 characters. ____________________________________________________________________ Medium-distance decision making strategies How many q statements are there? 42 2.1.1 Creating the Study Files 9:36 (0:15:01)..

12. 12 Enter the leftmost column value (e.g. -5): -4 Enter the rightmost column value (e.g. 5): 4 Enter the Number of Rows for each Column from -4 to 4. For Example: 2 3 3 4 4 4 3 3 2 : 2 3 5 7 8 7 5 3 2 Ready to process another sort. Enter one of the following codes: A - to add a new sort C - to change a previous sort D - to delete a sort S - to show a previous sort Q - to query status of this study X - to exit QENTER (stop entering/changing sorts) a 2.1.1 Creating the Study Files contd. 9:36 (0:15:07)0:12

13. 13 Enter identification code for subject no. 1 (A case label consisting of max. 8 characters) Anita NN Enter the Sort Values for Subject 1 Anita NN Enter the Statement Numbers, Separated by Spaces, for Column -4: 20 23 Enter the Statement Numbers, Separated by Spaces, for Column -3: 14 17 35 Enter the Statement Numbers, Separated by Spaces, for Column -2: 4 8 38 6 41 Enter the Statement Numbers, Separated by Spaces, for Column -1: 12 9 16 24 25 37 39 Enter the Statement Numbers, Separated by Spaces, for Column 0: 5 10 28 31 32 34 36 42 Enter the Statement Numbers, Separated by Spaces, for Column 1: 2 3 15 19 26 29 33 2.1.2 Entering the Data 9:36 (0:15:33)0:12

14. 14 (Continuation of Subject 1 Anita NN) Enter the Statement Numbers, Separated by Spaces, for Column 2: 7 13 18 21 30 Enter the Statement Numbers, Separated by Spaces, for Column 3: 1 22 40 Enter the Statement Numbers, Separated by Spaces, for Column 4: 11 22 -4 -3 -2 -1 0 1 2 3 4 !----!----!----!----!----!----!----!----!----! ! 20 ! 14 ! 4 ! 12 ! 5 ! 2 ! 7 ! 1 ! 11 ! !----!----!----!----!----!----!----!----!----! ! 23 ! 17 ! 8 ! 9 ! 10 ! 3 ! 13 ! 22 ! 22 ! !----!----!----!----!----!----!----!----!----! ! 35 ! 38 ! 16 ! 28 ! 15 ! 18 ! 40 ! !----!----!----!----!----!----!----! ! 6 ! 24 ! 31 ! 19 ! 21 ! !----!----!----!----!----! ! 41 ! 25 ! 32 ! 26 ! 30 ! !----!----!----!----!----! ! 37 ! 34 ! 29 ! !----!----!----! ! 39 ! 36 ! 33 ! !----!----!----! ! 42 ! !----! 2.1.2 Entering the Datacontd. 9:37 (0:15:48)0:13

15. 15 SubjNo: 1 ID: Anita NN The following statements have been entered more than once. 22 The following statements have not been entered 27 The sort must be re-entered. Look at the problems above and decide what column you want to modify first. Give the value of the column you want to change: 4 The current values for column 4 are: 11 22 Enter all of the new values, even ones that were good: 11 27 2.1.2 Entering the Datacontd. -4 -3 -2 -1 0 1 2 3 4 !----!----!----!----!----!----!----!----!----! ! 20 ! 14 ! 4 ! 12 ! 5 ! 2 ! 7 ! 1 ! 11 ! !----!----!----!----!----!----!----!----!----! ! 23 ! 17 ! 8 ! 9 ! 10 ! 3 ! 13 ! 22 ! 27 ! !----!----!----!----!----!----!----!----!----! ! 35 ! 38 ! 16 ! 28 ! 15 ! 18 ! 40 ! !----!----!----!----!----!----!----! ! 6 ! 24 ! 31 ! 19 ! 21 ! !----!----!----!----!----! ! 41 ! 25 ! 32 ! 26 ! 30 ! !----!----!----!----!----! ! 37 ! 34 ! 29 ! !----!----!----! ! 39 ! 36 ! 33 ! !----!----!----! ! 42 ! !----! 9:37 (0:16:03)0:13

16. 16 SubjNo: 1 ID: Anita NN The Sum is 0.00, and the Mean is 0.00, for Subject 1 Anita NN The Sort is OK, Do You Want to Change It Anyway? (y/N): n Do you want to enter another sort? (Y/n): n Ready to process another sort. Enter one of the following codes: A - to add a new sort C - to change a previous sort D - to delete a sort S - to show a previous sort Q - to query status of this study X - to exit QENTER (stop entering/changing sorts) x Current Project is... C:\PQMETHOD\projects\q-conference/travel Choose the number of the routine you want to run and enter it. 1 - STATES - Enter (or edit) the file of statements 2 - QENTER - Enter q sorts (new or continued) 3 - QCENT - Perform a Centroid factor analysis 4 - QPCA - Perform a Principal Components factor analysis 5 - QROTATE - Perform a manual rotation of the factors 6 - QVARIMAX - Perform a varimax rotation of the factors 7 - QANALYZE - Perform the final Q analysis of the rotated factors 8 - View project files travel.* X - Exit from PQMethod Last Routine Run Successfully - QENTER x Thank you for using PQMethod Press to exit 2.1.2 Entering the Datafinished 9:37 (0:16:14)0:14

17. 17 2.1.3 The PQMethod Study Files 9:38 (0:17:06)0:14*

18. 18 1 st header record: n sorts, n statements, and study title 2 nd header record: design specifications  Following are the data records 2.1.3 Study Files: travel.dat n Sorts n Statements 9:48 (0:27:05)0:15

19. 19 For output (travel.lis) max. 60 characters  2.1.3 Study Files: travel.sta 9:48 (0:27:07)0:16

20. 20 2.2 Analysis 1st Overview: Likely Number of Factors 2.2 Analysis 1st Overview: Likely Number of Factors 2.2.1 Eigenvalues 2.2.2 Factor Plot, Principal Components #1 vs. #2 2.2.3 Up to How Many Varimax Factors With at Least 2 or 3 Representatives (“Flags”)? 2.2.4 Intercorrelations Between Provisional Factor Scores 2.2.5 Considerations Related to Theoretical Expectations and Interpretability 2.2 Analysis: 1st Overview Outline 9:41 (0:20:06)0:17*

21. 21 Current Project is... C:\PQMETHOD\projects\q-conference/travel Choose the number of the routine you want to run and enter it. 1 - STATES - Enter (or edit) the file of statements 2 - QENTER - Enter q sorts (new or continued) 3 - QCENT - Perform a Centroid factor analysis 4 - QPCA - Perform a Principal Components factor analysis 5 - QROTATE - Perform a manual rotation of the factors 6 - QVARIMAX - Perform a varimax rotation of the factors 7 - QANALYZE - Perform the final Q analysis of the rotated factors 8 - View project files travel.* X - Exit from PQMethod Last Routine Run Successfully - (Initial) 4 Eigenvalues As Percentages Cumul. Percentages ----------- -------------- ------------------ 1 13.5325 34.6986 34.6986 2 6.4105 16.4371 51.1357 3 2.3483 6.0212 57.1570 2.2.1 Eigenvalues..continued on next slide 9:48 (0:27:11)0:18

22. 22 Last Routine Run Successfully - (Initial) 4 Eigenvalues As Percentages Cumul. Percentages ----------- -------------- ------------------ 1 13.5325 34.6986 34.6986 2 6.4105 16.4371 51.1357 3 2.3483 6.0212 57.1570 4 1.8683 4.7904 61.9474 5 1.7195 4.4089 66.3563 6 1.2740 3.2667 69.6230 7 1.2448 3.1919 72.8149 8 1.0907 2.7967 75.6116 9 1.0120 2.5948 78.2064 10 0.9255 2.3731 80.5796 11 0.8716 2.2350 82.8146 12 0.7750 1.9873 84.8018 13 0.7351 1.8849 86.6868 14 0.6157 1.5788 88.2656 15 0.5645 1.4476 89.7132 16 0.5012 1.2852 90.9983 2.2.1 Eigenvaluescontd. 9:48 (0:27:18)0:19

23. 23 -Very strong 1 st component (35% expl. Var.) -2 nd component only less than half of 1 st (16%) -Another steep drop to the 3 rd component (6%) -After that, tappering off with small bends after #5 and #7 2.2.1 EigenvaluesSummary  I would not bet on the existence of 2 or more distinct (=orthogonal, uncorrelated) points of view But let’s inspect the factor plot 9:48 (0:27:20)0:22

24. 24 2.2.2 Factor Plot #1 vs. #2 (Centroids) 9:49 (0:28:37)0:24*

25. 25 -Strong General Factor is not bipolar  Either P-set does not split into train vs. car partisans or  too many statements are indisputably either true or false for train and car travelers as well -Close to the typical “Umbrella” image, where Varimax axes will fall at 45 degrees  Varimax can distribute variance more evenly, but many sorts at intermediate positions Coming Next: Up to how many Varimax Factors with at least 2 or 3 Representatives (“Flags”)? 2.2.2 Factor Plot #1 vs. #2contd. 9:51 (0:30:19)0:25

26. 26 Varimax-Rotated Centroids with automatic „flags“ – 7 too many ! 2.2.3 Up to how many with 2 or 3 “flags”? 7? 9:52 (0:30:49)0:27

27. 27 Varimax-Rotated Centroids with automatic „flags“ – 5 Mmmh ? 2.2.3 Up to how many with 2 or 3 “flags”? 5? 9:52 (0:30:55)0:28

28. 28 Varimax Rotated Centroids with automatic „flags“ – 4 Maybe, that‘s it Now, let‘s look at the factor score intercorrelations ! 2.2.3 Up to how many with 2 or 3 “flags”? 4! 9:52 (0:31:22)0:28

29. 29 Correlations Between Factor Scores 1 2 3 4 1 1.0000 0.2990 0.4686 0.4181 2 0.2990 1.0000 0.5995 0.0813 3 0.4686 0.5995 1.0000 0.4464 4 0.4181 0.0813 0.4464 1.0000 PQMethod2.11 Medium-distance dec Path and Project Name: D:\konferenzen\q-conf08  Correlations not as low as would be desirable 2.2.4 Intercorrelations betw. Prov. Factor Scores 9:53 (0:31:48)0:29

30. 30 Considerations Related to Theoretical Expectations and Interpretability Typically not very explicitely documented in Q publications …. …. neither is there enough time for that in this talk 2.2.5 Theoretical expectations and interpretability 9:53 (0:32:34)0:29*

31. 31 2.3 Analysis: Choice of Solution – Fine Tuning 2.3 Analysis: Choice of Solution – Fine Tuning - Most time-consuming part of the analysis process -Judgemental rotation? -Carefully checking „flags“ (factor markers) -Provisional interpretation of provisional solution -Restarting process with another # of factors, another rotation … 2.3 Analysis: Choice of Solution – Fine Tuning - Job van Exel (et al.) decided for -4 instead of only 2 factors -Additional manual rotation (improvement doubtful) 9:55 (0:34:27)0:30*

32. 32 2.4 Results and Interpretation (Travel Study) 2.4 Results and Interpretation (Travel Study) -Next slide will show only small a sample of study results -The contents of PQMethod output will be explained later (MiL study) 2.4 Results and Interpretation Intro 9:55 (0:34:33)0:32*

33. 33 F1: Choice travelers with a car as dominant alternative F2: Choice travelers with a car preference F3: Choice travelers with a public transport preference F4: Conscious car dependent travelers Correlations F2 F3 F4 F1.64.50.60 F2.62.46 F3.14 2.4 Results and Interpretation (Travel Study) 10:40 (0:00:11)0:32

34. 34 3. More specific Questions and Solutions with PQMethod 3.1 Relating Q Factors to External Data (with Travel-Study Example) 3.2 Splitting up a Bipolar Factor into Its Two Poles (Mother-in-Law Study by Andrea Kettenbach) 3.3 Splitting and Combining Data from Different P-Samples 3. More specific Questions and SolutionsIntro 10:49 (0:06:47)0:35*

35. 35 3.1 Relating Q Factors to External Data (with Travel-Study Example) 3.1.1 The traditional, nominal approach: Factors categorize people 3.1.2 The quantitative alternative: Factor loading coefficients as measures 3.1 Relating Q Factors to External Data Intro 10:49 (0:06:58)0:35

36. 36 The traditional, nominal approach: Factors categorize people Significance tests: Car ownership: p <.01 - Intercity r station: n.s. 3.1.1 The traditional approach F1 Dominant Car F2 Car Preference F3 Train Prefence F4 Car Dependent Table 4. Demographic data of interest 10:54 (0:11:55)0:36

37. 37 The quantitative alternative: Factor loading coefficients as measures * travel_loadings.sps. DATA LIST / sort 5-12 (a) car 11 (a) pubtrans 12 (a) a1 17-23 a2 27-33 a3 37-43 a4 47-53. begin data 1 Anita NN 0.6175X 0.0131 0.1741 0.0633 2 Anke PY 0.6304X 0.1352 0.3173 0.2659 3 Anna PN 0.0861 0.4308 0.6386X 0.1662 4 Arjan PN 0.1643 0.4468 0.2691 0.2008 5 Bened PN 0.3643 0.0000 0.2950 0.6026X 6 Bob PN 0.3660 0.3474 0.5330X 0.4069 7 Dani LN -0.0865 0.2659 -0.1869 0.6296X 8 DrkJK LY 0.0644 0.3512 0.1242 0.6432X 9 DrkJM PY 0.2753 0.1443 0.2817 0.7075X... end data. MEANS TABLES=a1 to a4 BY car /CELLS MEAN COUNT STDDEV /STATISTICS ANOVA. 3.1.2 The Quantitative Alternative SPSS Syntax File 10:54 (0:11:56)0:38

38. 38 The quantitative alternative: Factor loading coefficients as measures 3.1.2 The Quantitative Alternative contd. p >.10, eta =.20 p <.05, eta =.45 p <.001, eta =.59 p <.001, eta =.71 10:55 (0:12:40)0:40

39. 39 Intercity train availability.. -Is unrelated to all 4 factors (correlations close to Zero) 3.1.2 The Quantitative Alternative contd. The quantitative alternative: Factor loading coefficients as measures 10:58 (0:00:04)0:42

40. 40 Example for the Case of a Bipolar Factor: Mother-in-Law Study by Andrea Kettenbach (Dissertation project, in progress) 3.2.1 MiL Study Introduction 3.2.2 Determining the Factor Solution 3.2.3 Splitting Up the Bipolar Factor 3.2.4 QANALYZE the Results 3.2 Splitting Up a Bipolar Factor into Its 2 Poles Mother-in-Law (MiL) Study 10:58 (0:00:02)0:43*

41. 3.2.1 MiL Study Introduction 41 She cares lovingly for the family... is an affectionate granny... is always there for the children... is open-minded... is not obtrusive... is cheerful... is a good listener. …... gives unsolicited advice... is very offish... is too curious... knows everything better... nags about the housekeeping... is beastly to me... is guileful and deals in an underhanded manner... nags all day long.  34 women were interviewed about their relation to their mother-in-law, in addition, they q-sorted 54 short statements, like the following: 10:59 (0:01:49)0:43

42. 42 Eigenvalues and Plot Principal Components #1 vs. #2 3.2.2 MiL Study: Determining the Factor Solution 1 As Percentages -------------- 1 40.3495 2 10.8541 3 7.5711 4 4.3637 5 3.7587 6 3.5123 7 3.4131 8 3.0142 9 2.7318 10 2.2085 11 2.0040 12 1.9017 13 1.7619 14 1.6240 15 1.5064 16 1.3969 - Strong 1st PC is bipolar - Eigenvalues: Gap after #3 11:00 (0:02:32)0:45

43. 43 3 and 4 Varimax-Rotated Components with „auto-flags“ 3.2.2 MiL Study: Determining the Factor Solution 2 Three is OK!Four is too many 11:00 (0:02:52)0:46

44. 44 Steps to accomplish splitting up the factor #1: 3.2.3 MiL Study: Splitting up the Bipolar Factor Intro 1) Start with QVARIMAX, 3 factors, loaded in PQROT, and flag factors 2) Save factor numbers: 1 1 2 3 3) Reload saved four factors 4) Invert new factor #2 (copy of previous #1) 5) Remove all „-“ flags for factors 1 und 2 6) Save factors and run QANALYZE For these final analyses, the.dat file was reordered according to MiL’s “grade” 11:02 (0:04:10)0:47

45. 45 3.2.2 MiL Study: Splitting up the Bipolar Factor 1) 1) Start with QVARIMAX, 3 factors, loaded in PQROT, and flag factors Sorted by „Grades“ 11:02 (0:04:51)0:48

46. 46 3.2.2 MiL Study: Splitting up the Bipolar Factor 2) 2) Save factor numbers: 1 1 2 3 11:02 (0:04:55)0:49

47. 47 3.2.2 MiL Study: Splitting up the Bipolar Factor 3) 3) Reload saved four factors 11:03 (0:05:00)0:49

48. 48 3.2.2 MiL Study: Splitting up the Bipolar Factor 4) 4) Invert new factor #2 (copy of previous #1) 11:03 (0:05:17)0:50

49. 49 3.2.2 MiL Study: Splitting up the Bipolar Factor 5) 5) Remove all „-“ flags for factors 1 und 2 11:03 (0:05:57)0:51

50. 50 3.2.2 MiL Study: Splitting up the Bipolar Factor 6) ….. and run QANALYZE 6) Save factors … 11:04 (0:06:04)0:51

51. 51 3.2.4 QANALYZE the Results (MiL.LIS) 3.2.4 QANALYZE the Results (MiL.LIS) 3.2.4.1 Contents of the.lis File 3.2.4.3 Factor Q-Sort Values (in MS-Word) 3.2.4.2 The Four Factor-Prototype Sorts 3.2.4.4 Some additional bits of clarification from the.lis 3.2.4 QANALYZE the Results Intro 11:04 (0:06:54)0:51*

52. 52 Overview of Tables: Correlation matrix between sorts Unrotated factor loading matrix Cumulative communalities matrix Rotated factor loading matrix Statistics of individual sorts (Mean, Sd) Factor score matrix (Z-scores and ranks) Correlations between factor scores For every factor in turn: Statements sorted by Z-score For every pair of factors: Statements sorted by difference 3.2.4.1 Contents of the.lis file Quite voluminous. Correlations Between Factor Scores 1 2 3 4 1 1.0000 -0.8601 0.1911 -0.2098 2 -0.8601 1.0000 0.0870 0.2939 3 0.1911 0.0870 1.0000 -0.0392 4 -0.2098 0.2939 -0.0392 1.0000 11:08 (0:02:38)0:52

53. 53 Overview of Tables (contd.) Factor Q-Sort Values (in „pile scaling“) Same table sorted by Consensus vs. Disagreement Factor characteristics, like „No. of defining variables“ Distinguishing statements per factor Consensus statements 3.2.4.1 Contents of the.lis File contd. Bulk of loquacious output are variations of the same basic content: Its core message consists in the Factor-Prototype Sorts: Factor Q-Sort Values (in „pile scaling“) 11:11 (0:05:51)0:54 Disclosing a secret little trick here … I changed „pile scaling“ from -2 thru +2  -5 thru +5 in the 2 nd header record before running QANALYZE

54. 54 Make it a table in MS-Word …. 3.2.4.2 Factor Q-Sort Values (Protoype Sorts) 11:12 (0:06:17)0:56*

55. 55 3.2.4.2 Factor Q-Sort Values (in MS-Word) 11:12 (0:07:01)0:57

56. 56 3.2.4.3 Prototype Sorts F1+ Fabulous MiL 11:15 (0:09:35)0:58

57. 57 3.2.4.3 Prototype Sorts F1- Dreadful MiL 11:15 (0:10:01)1:00

58. 58 3.2.4.3 F2 Annoying but sweet and helpful MiL 11:19 (0:14:10)1:01

59. 59 3.2.4.3 F3 Distant, touchy and cold-hearted MiL 11:20 (0:14:45)1:02

60. 60 3.2.4.4 Additional bits of clarification Some additional bits of clarification to be gleaned from the MiL.lis file  Distinguishing Statements, e.g.  Z-Scores  More precise, and preserve form of distribution. Example:  F2- / Dreadful MiL : Statements on top more extreme than bottommost statements. Z-Scores for Factor F1 – Dreadful MiL 6She is annoying. 1.741 4She interferes. 1.571 21She knows things better. 1.571 12She nags all day long. 1.513 3She knows everything better. 1.508 25She is guileful and deals in an underhanded manner. 1.508 24She gets easily offended. 1.414  Bottommost less descriptive, less unanimity: 35She remains neutral.-1.231 33She has a wide range of interests.-1.231 30She shows large interest in the well-being of the f-1.249 47She is sympathetic.-1.379 11:24 (0:18:36)1:04*

61. 61 3.2.4.4 Additional bits of clarification contd.  Consensus statements  There is only one consensus statement: 11:25 (0:19:24)1:06

62. 62 3.3 Splitting and Combining Data from Different P-Samples 3.3.1 Types of Research Approaches and Problems to Be Solved 3.3.2 Handling Q-Sort Data Sets in PQMethod 3.3 Splitting and Combining Data …Intro 11:25 (0:19:43)1:07

63. 63 3.3.1. Types of Approaches and Problems  Splitting the P set set into sub-samples for separate analyses  Identifying factors with a small (theoretical, structured) sub-sample  Merging data from different studies that use the same Q sample 11:28 (0:22:18)1:08

64. 64 3.3.1. Types of Approaches and Problems contd.  Excursus: The meaning of “Secondary Factor Analysis”  Comparing Q-factor structures between P samples  Correlating sets of Q-factor scores (factor prototype sorts)  “Spiking” a data set with factor scores from another sample  Before-after type of designs  Combined analysis (shared factor solution)  Separate analyses (focus on differing factor structures)  cf. Comparing Q-factor structures between P samples  Sets of factor loadings as quantitative measures 11:33 (0:27:57)1:10

65. 65 3.3.2 Handling Q-Sort Data Sets in PQMethod As I told you already ……. 11:34 (0:28:34)1:14

66. 66 3.3.2 Handling Q-Sort Data Sets in PQMethodcontd. …. use the Editor for managing PQMethod data 11:34 (0:28:43)1:16

67. 67 3.3.2 Handling Q-Sort Data Sets in PQMethodcontd. How do I get the Factor Prototypes into the.dat file? 11:34 (0:28:58)1:17

68. 68 3.3.2 Handling Q-Sort Data Sets in PQMethodcontd. File fax.dat: 11:35 (0:30:04)1:17

69. 69 4. Questions ? and Discussions 11:35 (0:30:07)1:20 Questions ?

70. The End Thank you for your patience