Common and Specific Approaches in the Analysis of Q-Sort Data with PQMethod Peter Schmolck Universität der Bundeswehr München by OfficeOne: AutoDateTime

Common and Specific ApproachesCommon and Specific Approachesin the Analysis of Q-Sort Datain the Analysis of Q-Sort Data

withwith PQMPQMethodethod

Peter SchmolckPeter SchmolckUniversität der Bundeswehr München

by OfficeOne: AutoDateTime9:24 (0:02:59)

2

OutlineOutline

Outline

1. HISTORY

2. TYPICAL STEPS OF ANALYSISJOB’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

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Outline contd.

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)

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1. History 1. History 1. History 1. History

1. History Intro

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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

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10 Aug 1994 10 Aug 1994 Peter Schmolck announced PC Version of Atkinson’s QMETHOD

- downloadable from WWW homepage

09 Aug 199609 Aug 1996 “QMethod Page”

“http://www.rz.unibw-muenchen.de/~p41bsmk/qmethod” - still OK (but also, e.g. schmolck.org/qmethod)

1. History contd.

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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. History contd.

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

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2. Typical Steps of 2. Typical Steps of AnalysisAnalysis

Job‘s Travel-Study Job‘s Travel-Study ExampleExample

2. Typical Steps of 2. Typical Steps of AnalysisAnalysis

Job‘s Travel-Study Job‘s Travel-Study ExampleExample

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 Analysis Outline

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Job‘s OS Publication on which the following demonstrations are based

2. Typical Steps .. Travel Study Example

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C:\PQMETHOD\projects\q-conference>pqmethod travel +---------------------------------------------------+ | PQMethod - 2.11 | | (November 2002) | +---------------------------------------------------| | by [email protected] | | 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 Files Intro

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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

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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.

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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

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(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 Data contd.

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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 Data contd.

-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 ! !----!

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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 - QENTERx Thank you for using PQMethod Press <ENTER> to exit

2.1.2 Entering the Data finished

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2.1.3 The PQMethod Study Files

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1st header record: n sorts, n statements, and study title

2nd header record: design specifications

Following are the data records

2.1.3 Study Files: travel.dat

n Sorts n Statements

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For output (travel.lis) max. 60 characters

2.1.3 Study Files: travel.sta

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2.2 Analysis2.2 Analysis1st Overview: 1st Overview:

Likely Number of FactorsLikely Number of Factors

2.2 Analysis2.2 Analysis1st Overview: 1st Overview:

Likely Number of FactorsLikely 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

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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

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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 Eigenvalues contd.

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- Very strong 1st component (35% expl. Var.)

- 2nd component only less than half of 1st (16%)

- Another steep drop to the 3rd component (6%)

- After that, tappering off with small bends after #5 and #7

2.2.1 Eigenvalues Summary

I would not bet on the existence of 2 or more distinct (=orthogonal, uncorrelated) points of view

But let’s inspect the factor plot

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2.2.2 Factor Plot #1 vs. #2 (Centroids)

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- 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. #2 contd.

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Varimax-Rotated Centroids with automatic „flags“ – 7 too many !

2.2.3 Up to how many with 2 or 3 “flags”? 7?

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Varimax-Rotated Centroids with automatic „flags“ – 5 Mmmh ?

2.2.3 Up to how many with 2 or 3 “flags”? 5?

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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!

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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 decPath and Project Name: D:\konferenzen\q-conf08

Correlations not as low as would be desirable

2.2.4 Intercorrelations betw. Prov. Factor Scores

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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

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2.3 Analysis:2.3 Analysis:Choice of Solution –Choice of Solution –

Fine TuningFine Tuning

2.3 Analysis:2.3 Analysis:Choice of Solution –Choice of Solution –

Fine TuningFine 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)

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2.4 Results and 2.4 Results and InterpretationInterpretation(Travel Study)(Travel Study)

2.4 Results and 2.4 Results and InterpretationInterpretation(Travel Study)(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

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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)

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3. 3. More specific Questions More specific Questions and Solutions with and Solutions with

PQMethodPQMethod

3. 3. More specific Questions More specific Questions and Solutions with and Solutions with

PQMethodPQMethod

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

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3.1 3.1 RRelating Q Factors elating Q Factors to External Data to External Data

(with Travel-Study Example)(with Travel-Study Example)

3.1 3.1 RRelating Q Factors elating Q Factors to External Data to External Data

(with Travel-Study Example)(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

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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 Pref

ence

F4 Car Dependent

Table 4. Demographic data of interest

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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

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3.1.2 The Quantitative Alternative contd.

p > .10 , eta = .20

p < .05, eta = .45

p < .001, eta = .59

p < .001, eta = .71

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Intercity train availability ..- Is unrelated to all 4 factors

(correlations close to Zero)

3.1.2 The Quantitative Alternative contd.


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Example for the Case of a Bipolar Factor:

Mother-in-Law Study by Andrea Kettenbach

(Dissertation project, in progress)

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

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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:

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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

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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

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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”

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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“

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2) Save factor numbers: 1 1 2 3

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3) Reload saved four factors

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4) Invert new factor #2 (copy of previous #1)

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5) Remove all „-“ flags for factors 1 und 2

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….. and run QANALYZE

6) Save factors …

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3.2.4 QANALYZE 3.2.4 QANALYZE the Results the Results

(MiL.LIS)(MiL.LIS)

3.2.4 QANALYZE 3.2.4 QANALYZE the Results the Results

(MiL.LIS)(MiL.LIS)

3.2.4.1 Contents of the <study>.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 <study>.lis

3.2.4 QANALYZE the Results Intro

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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 <study>.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

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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 <study>.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“)

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• Disclosing a secret little trick here …

• I changed „pile scaling“ from -2 thru +2 -5 thru +5 in the 2nd header record before running QANALYZE

54Make it a table in MS-Word ….

3.2.4.2 Factor Q-Sort Values (Protoype Sorts)

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3.2.4.2 Factor Q-Sort Values (in MS-Word)

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3.2.4.3 Prototype Sorts F1+ Fabulous MiL

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3.2.4.3 Prototype Sorts F1- Dreadful MiL

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3.2.4.3 F2 Annoying but sweet and helpful MiL

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3.2.4.3 F3 Distant, touchy and cold-hearted MiL

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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

6 She is annoying. 1.741 4 She interferes. 1.57121 She knows things better. 1.57112 She nags all day long. 1.513 3 She knows everything better. 1.50825 She is guileful and deals in an underhanded manner. 1.50824She gets easily offended. 1.414

Bottommost less descriptive, less unanimity:35 She remains neutral. -1.23133 She has a wide range of interests. -1.23130 She shows large interest in the well-being of the f -1.24947 She is sympathetic. -1.379

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3.2.4.4 Additional bits of clarification contd.

Consensus statements

There is only one consensus statement:

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3.3Splitting and Combining

Data from Different P-Samples

3.3Splitting 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

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3.3.1. Types of Approaches and Problems

Splitting the P set set into sub-samples for separateanalyses

Identifying factors with a small (theoretical, structured) sub-sample

Merging data from different studies that use the same Q sample

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3.3.1. Types of Approaches and Problemscontd.

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

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3.3.2 Handling Q-Sort Data Sets in PQMethod

As I told you already …….

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3.3.2 Handling Q-Sort Data Sets in PQMethodcontd.

…. use the Editor for managing PQMethod data

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How do I get the Factor Prototypes into the .dat file?

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File fax.dat:

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4.4.Questions ?Questions ?

and Discussionsand Discussions

4.4.Questions ?Questions ?

and Discussionsand Discussions

11:35 (0:30:07) 1:20

Questions ?

The End

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Common and Specific Approaches in the Analysis of Q-Sort Data with PQMethod Peter Schmolck Universität der Bundeswehr München by OfficeOne: AutoDateTime