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Using the qmethod package in R for analysis
Christoph Schulze
2026-06-19
What is R?
What is this & who did this?
This package is made by Zabala (2014)
- not for collecting data
- not meant for analysing qualitative q data
- but for analysis of quantitative q data
- how many factors are in my dataset?
- which respondents belong to a factor?
- how strong is a respondents adhering to a specific factor?
- what are consensus and what are distinguishing statements?
- which statements characterise a factor?
Getting into the code
Loading data
1library(qmethod)
#before importing data, delete in excel first column and save as csv file
2qstake <- read.csv("01_data/qstake.csv", header=TRUE, sep=";",dec=".")
#for some reason line 43 are pure na's. next command will delete na's of the dataframe qdata
qstake <-na.omit(qstake)
#look into correlation of q-sets
3cor(qstake)- 1
- Loading package
- 2
- Loading data
- 3
- Checking correlations among Q-sorts
Extracting factors
Q-method analysis.
Finished on: Fri Jun 19 16:11:10 2026
'qmethod' package version: 1.8.4
Original data: 38 statements, 25 Q-sorts
Forced distribution: TRUE
Number of factors: 3
Extraction: PCA
Rotation: varimax
Flagging: automatic
Correlation coefficient: spearmanExtracting factors
f1 f2 f3
Resp1 0.14 0.81 0.11
Resp2 0.18 0.59 0.40
Resp3 0.79 0.19 0.26
Resp4 0.39 0.45 0.23
Resp5 0.61 0.64 -0.02
Resp6 0.62 0.53 0.06
Resp7 0.22 0.72 0.07
Resp8 0.66 0.09 0.32
Resp9 0.37 0.33 0.61
Resp10 0.52 0.56 0.10
Resp11 0.25 0.40 0.44
Resp12 0.70 0.23 0.25
Resp13 0.57 0.12 0.54
Resp14 0.47 0.08 0.33
Resp15 0.64 0.20 0.02
Resp16 0.67 0.03 0.40
Resp17 0.03 0.84 0.10
Resp18 0.50 0.22 0.14
Resp19 0.52 0.52 0.14
Resp20 0.28 0.05 0.70
Resp21 -0.10 0.40 0.69
Resp22 0.61 0.35 0.28
Resp23 0.23 -0.06 0.68
Resp24 0.45 0.51 0.33
Resp25 0.32 0.42 0.46Extracting factors
flag_f1 flag_f2 flag_f3
Resp1 FALSE TRUE FALSE
Resp2 FALSE TRUE FALSE
Resp3 TRUE FALSE FALSE
Resp4 FALSE TRUE FALSE
Resp5 FALSE TRUE FALSE
Resp6 TRUE FALSE FALSE
Resp7 FALSE TRUE FALSE
Resp8 TRUE FALSE FALSE
Resp9 FALSE FALSE TRUE
Resp10 FALSE TRUE FALSE
Resp11 FALSE FALSE FALSE
Resp12 TRUE FALSE FALSE
Resp13 TRUE FALSE FALSE
Resp14 TRUE FALSE FALSE
Resp15 TRUE FALSE FALSE
Resp16 TRUE FALSE FALSE
Resp17 FALSE TRUE FALSE
Resp18 TRUE FALSE FALSE
Resp19 FALSE FALSE FALSE
Resp20 FALSE FALSE TRUE
Resp21 FALSE FALSE TRUE
Resp22 TRUE FALSE FALSE
Resp23 FALSE FALSE TRUE
Resp24 FALSE FALSE FALSE
Resp25 FALSE FALSE FALSEExtracting factors
fg1 f1 fg2 f2 fg3 f3
Resp1 0.14 * 0.81 0.11
Resp2 0.18 * 0.59 0.40
Resp3 * 0.79 0.19 0.26
Resp4 0.39 * 0.45 0.23
Resp5 0.61 * 0.64 -0.02
Resp6 * 0.62 0.53 0.06
Resp7 0.22 * 0.72 0.07
Resp8 * 0.66 0.09 0.32
Resp9 0.37 0.33 * 0.61
Resp10 0.52 * 0.56 0.10
Resp11 0.25 0.40 0.44
Resp12 * 0.70 0.23 0.25
Resp13 * 0.57 0.12 0.54
Resp14 * 0.47 0.08 0.33
Resp15 * 0.64 0.20 0.02
Resp16 * 0.67 0.03 0.40
Resp17 0.03 * 0.84 0.10
Resp18 * 0.50 0.22 0.14
Resp19 0.52 0.52 0.14
Resp20 0.28 0.05 * 0.70
Resp21 -0.10 0.40 * 0.69
Resp22 * 0.61 0.35 0.28
Resp23 0.23 -0.06 * 0.68
Resp24 0.45 0.51 0.33
Resp25 0.32 0.42 0.46Looking at factors
- number of Q-sorts
- Size of Eigenvalues
- Share of variacne explained
av_rel_coef nload eigenvals expl_var reliability se_fscores
f1 0.8 10 5.786761 23.14705 0.9756098 0.1561738
f2 0.8 7 4.900121 19.60048 0.9655172 0.1856953
f3 0.8 4 3.486081 13.94432 0.9411765 0.2425356[1] 0.3244428Humphrey’s rule:
- select to highest two factor loadings and multiply these
- compare product to humphreys critical value
Looking at factors
Looking at factors
Q-method analysis.
Finished on: Fri Jun 19 16:11:10 2026
'qmethod' package version: 1.8.4
Original data: 38 statements, 25 Q-sorts
Forced distribution: TRUE
Number of factors: 3
Extraction: PCA
Rotation: varimax
Flagging: automatic
Correlation coefficient: spearman
Factor scores
fsc_f1 fsc_f2 fsc_f3
1 -2 0 3
2 -4 -4 -2
3 0 -2 -3
4 -1 3 0
5 3 3 1
6 -3 -3 -4
7 0 2 0
8 1 2 4
9 0 0 2
10 -2 0 -1
11 1 -1 2
12 2 0 3
13 3 1 -2
14 -1 3 -1
15 1 -2 4
16 -2 -3 -2
17 -4 1 -4
18 2 1 1
19 4 0 0
20 0 2 0
21 1 2 1
22 2 4 1
23 1 4 3
24 -1 -1 0
25 1 0 -1
26 -3 -4 -3
27 3 1 1
28 -1 -1 0
29 -3 -2 -1
30 4 1 2
31 0 0 1
32 2 -1 -1
33 0 1 -1
34 -2 -1 2
35 0 0 0
36 -1 -1 -2
37 -1 -3 -3
38 0 -2 0
f1 f2 f3
Average reliability coefficient 0.80 0.80 0.80
Number of loading Q-sorts 10.00 7.00 4.00
Eigenvalues 5.79 4.90 3.49
Percentage of explained variance 23.15 19.60 13.94
Composite reliability 0.98 0.97 0.94
Standard error of factor scores 0.16 0.19 0.24Looking at statement evaluations
Note:
- this graph is super helpful, but might appear too small in your window
- in RStudio you can click on Zoom to extend it and export it in better quality on your computer
Looking at statement evaluations
scores <- cbind(round(results$zsc, digits=2), results$zsc_n)
nfactors <- ncol(results$zsc)
col.order <- as.vector(rbind(1:nfactors, (1:nfactors)+nfactors))
scores <- as.data.frame(scores)
scores zsc_f1 zsc_f2 zsc_f3 fsc_f1 fsc_f2 fsc_f3
1 -0.76 -0.20 1.69 -2 0 3
2 -2.04 -1.75 -1.15 -4 -4 -2
3 0.03 -0.97 -1.52 0 -2 -3
4 -0.62 1.43 0.01 -1 3 0
5 1.12 1.17 0.61 3 3 1
6 -1.61 -1.61 -1.93 -3 -3 -4
7 0.39 1.16 0.42 0 2 0
8 0.87 1.10 1.80 1 2 4
9 0.49 -0.14 0.82 0 0 2
10 -1.06 0.15 -0.55 -2 0 -1
11 0.56 -0.80 0.85 1 -1 2
12 1.08 0.01 1.02 2 0 3
13 1.23 0.81 -0.87 3 1 -2
14 -0.55 1.36 -0.53 -1 3 -1
15 0.66 -0.90 1.77 1 -2 4
16 -0.75 -1.23 -1.18 -2 -3 -2
17 -1.82 0.49 -1.97 -4 1 -4
18 0.87 0.30 0.65 2 1 1
19 1.52 0.11 0.52 4 0 0
20 0.43 0.96 -0.14 0 2 0
21 0.66 1.08 0.76 1 2 1
22 0.99 1.50 0.66 2 4 1
23 0.75 1.68 0.93 1 4 3
24 -0.56 -0.80 0.20 -1 -1 0
25 0.61 0.30 -0.33 1 0 -1
26 -1.76 -1.88 -1.23 -3 -4 -3
27 1.24 0.63 0.58 3 1 1
28 -0.61 -0.54 0.12 -1 -1 0
29 -1.51 -1.11 -0.54 -3 -2 -1
30 1.36 0.91 0.85 4 1 2
31 -0.17 0.02 0.60 0 0 1
32 1.09 -0.38 -0.51 2 -1 -1
33 0.01 0.51 -0.30 0 1 -1
34 -1.00 -0.41 0.93 -2 -1 2
35 -0.22 -0.06 -0.26 0 0 0
36 -0.72 -0.28 -0.98 -1 -1 -2
37 -0.50 -1.69 -1.60 -1 -3 -3
38 0.26 -0.93 -0.24 0 -2 0Looking at statement evaluations
zsc_f1 zsc_f2 zsc_f3 fsc_f1 fsc_f2 fsc_f3
19 1.52 0.11 0.52 4 0 0
30 1.36 0.91 0.85 4 1 2
27 1.24 0.63 0.58 3 1 1
13 1.23 0.81 -0.87 3 1 -2
5 1.12 1.17 0.61 3 3 1
32 1.09 -0.38 -0.51 2 -1 -1
12 1.08 0.01 1.02 2 0 3
22 0.99 1.50 0.66 2 4 1
8 0.87 1.10 1.80 1 2 4
18 0.87 0.30 0.65 2 1 1
23 0.75 1.68 0.93 1 4 3
15 0.66 -0.90 1.77 1 -2 4
21 0.66 1.08 0.76 1 2 1
25 0.61 0.30 -0.33 1 0 -1
11 0.56 -0.80 0.85 1 -1 2
9 0.49 -0.14 0.82 0 0 2
20 0.43 0.96 -0.14 0 2 0
7 0.39 1.16 0.42 0 2 0
38 0.26 -0.93 -0.24 0 -2 0
3 0.03 -0.97 -1.52 0 -2 -3
33 0.01 0.51 -0.30 0 1 -1
31 -0.17 0.02 0.60 0 0 1
35 -0.22 -0.06 -0.26 0 0 0
37 -0.50 -1.69 -1.60 -1 -3 -3
14 -0.55 1.36 -0.53 -1 3 -1
24 -0.56 -0.80 0.20 -1 -1 0
28 -0.61 -0.54 0.12 -1 -1 0
4 -0.62 1.43 0.01 -1 3 0
36 -0.72 -0.28 -0.98 -1 -1 -2
16 -0.75 -1.23 -1.18 -2 -3 -2
1 -0.76 -0.20 1.69 -2 0 3
34 -1.00 -0.41 0.93 -2 -1 2
10 -1.06 0.15 -0.55 -2 0 -1
29 -1.51 -1.11 -0.54 -3 -2 -1
6 -1.61 -1.61 -1.93 -3 -3 -4
26 -1.76 -1.88 -1.23 -3 -4 -3
17 -1.82 0.49 -1.97 -4 1 -4
2 -2.04 -1.75 -1.15 -4 -4 -2Looking at statement evaluations
dist.and.cons f1_f2 sig_f1_f2 f1_f3 sig_f1_f3
1 Distinguishes all -0.559082411 * -2.44386587 6*
2 Distinguishes f3 only -0.283234506 -0.89040830 **
3 Distinguishes f1 only 0.995314755 *** 1.54750616 6*
4 Distinguishes all -2.041439221 6* -0.62109370 *
5 Consensus -0.056957901 0.50691520
6 Consensus 0.003736641 0.32043042
7 Distinguishes f2 only -0.769188781 ** -0.03345005
8 Distinguishes f3 only -0.228645117 -0.92606143 **
9 Distinguishes f2 only 0.636665572 ** -0.32968573
10 Distinguishes f2 only -1.210575598 6* -0.51072765
11 Distinguishes f2 only 1.364319205 6* -0.28682766
12 Distinguishes f2 only 1.073924970 *** 0.06031453
13 Distinguishes f3 only 0.421430216 2.09975345 6*
14 Distinguishes f2 only -1.910097464 6* -0.02219517
15 Distinguishes all 1.556712825 6* -1.11393550 ***
16 0.479643272 * 0.43095525
17 Distinguishes f2 only -2.305283405 6* 0.14662743
18 0.567198560 * 0.21970559
19 Distinguishes f1 only 1.412063581 6* 1.00022324 ***
20 Distinguishes f2 only -0.529633929 * 0.56132200
21 Consensus -0.421235496 -0.10463314
22 Distinguishes f2 only -0.506770944 * 0.33013467
23 Distinguishes f2 only -0.926023165 *** -0.18412624
24 Distinguishes f3 only 0.240931641 -0.75283163 **
25 Distinguishes f3 only 0.311217570 0.93757606 **
26 0.121704695 -0.53266171
27 Distinguishes f1 only 0.613421997 * 0.65941767 *
28 Distinguishes f3 only -0.066639363 -0.72543252 *
29 -0.401710135 -0.96688685 ***
30 Consensus 0.451444563 0.51047007
31 -0.193616194 -0.77237602 **
32 Distinguishes f1 only 1.471020475 6* 1.60258125 6*
33 Distinguishes f2 only -0.504911694 * 0.30758292
34 Distinguishes all -0.589385619 * -1.92590238 6*
35 Consensus -0.158946942 0.03653227
36 -0.441912029 0.25508654
37 Distinguishes f1 only 1.191681949 6* 1.10432815 ***
38 Distinguishes f2 only 1.192857427 6* 0.50563870
f2_f3 sig_f2_f3
1 -1.88478346 6*
2 -0.60717380 *
3 0.55219141
4 1.42034552 ***
5 0.56387310
6 0.31669378
7 0.73573873 *
8 -0.69741632 *
9 -0.96635130 **
10 0.69984795 *
11 -1.65114687 6*
12 -1.01361044 ***
13 1.67832324 6*
14 1.88790230 6*
15 -2.67064833 6*
16 -0.04868802
17 2.45191084 6*
18 -0.34749297
19 -0.41184035
20 1.09095593 ***
21 0.31660236
22 0.83690561 **
23 0.74189692 *
24 -0.99376327 **
25 0.62635849 *
26 -0.65436640 *
27 0.04599567
28 -0.65879316 *
29 -0.56517672
30 0.05902551
31 -0.57875982
32 0.13156078
33 0.81249461 **
34 -1.33651676 ***
35 0.19547921
36 0.69699857 *
37 -0.08735380
38 -0.68721872 *Looking at statement evaluations
dist.and.cons f1_f2 sig_f1_f2 f1_f3 sig_f1_f3
15 Distinguishes all 1.556712825 6* -1.11393550 ***
32 Distinguishes f1 only 1.471020475 6* 1.60258125 6*
19 Distinguishes f1 only 1.412063581 6* 1.00022324 ***
11 Distinguishes f2 only 1.364319205 6* -0.28682766
38 Distinguishes f2 only 1.192857427 6* 0.50563870
37 Distinguishes f1 only 1.191681949 6* 1.10432815 ***
12 Distinguishes f2 only 1.073924970 *** 0.06031453
3 Distinguishes f1 only 0.995314755 *** 1.54750616 6*
9 Distinguishes f2 only 0.636665572 ** -0.32968573
27 Distinguishes f1 only 0.613421997 * 0.65941767 *
18 0.567198560 * 0.21970559
16 0.479643272 * 0.43095525
30 Consensus 0.451444563 0.51047007
13 Distinguishes f3 only 0.421430216 2.09975345 6*
25 Distinguishes f3 only 0.311217570 0.93757606 **
24 Distinguishes f3 only 0.240931641 -0.75283163 **
26 0.121704695 -0.53266171
6 Consensus 0.003736641 0.32043042
5 Consensus -0.056957901 0.50691520
28 Distinguishes f3 only -0.066639363 -0.72543252 *
35 Consensus -0.158946942 0.03653227
31 -0.193616194 -0.77237602 **
8 Distinguishes f3 only -0.228645117 -0.92606143 **
2 Distinguishes f3 only -0.283234506 -0.89040830 **
29 -0.401710135 -0.96688685 ***
21 Consensus -0.421235496 -0.10463314
36 -0.441912029 0.25508654
33 Distinguishes f2 only -0.504911694 * 0.30758292
22 Distinguishes f2 only -0.506770944 * 0.33013467
20 Distinguishes f2 only -0.529633929 * 0.56132200
1 Distinguishes all -0.559082411 * -2.44386587 6*
34 Distinguishes all -0.589385619 * -1.92590238 6*
7 Distinguishes f2 only -0.769188781 ** -0.03345005
23 Distinguishes f2 only -0.926023165 *** -0.18412624
10 Distinguishes f2 only -1.210575598 6* -0.51072765
14 Distinguishes f2 only -1.910097464 6* -0.02219517
4 Distinguishes all -2.041439221 6* -0.62109370 *
17 Distinguishes f2 only -2.305283405 6* 0.14662743
f2_f3 sig_f2_f3
15 -2.67064833 6*
32 0.13156078
19 -0.41184035
11 -1.65114687 6*
38 -0.68721872 *
37 -0.08735380
12 -1.01361044 ***
3 0.55219141
9 -0.96635130 **
27 0.04599567
18 -0.34749297
16 -0.04868802
30 0.05902551
13 1.67832324 6*
25 0.62635849 *
24 -0.99376327 **
26 -0.65436640 *
6 0.31669378
5 0.56387310
28 -0.65879316 *
35 0.19547921
31 -0.57875982
8 -0.69741632 *
2 -0.60717380 *
29 -0.56517672
21 0.31660236
36 0.69699857 *
33 0.81249461 **
22 0.83690561 **
20 1.09095593 ***
1 -1.88478346 6*
34 -1.33651676 ***
7 0.73573873 *
23 0.74189692 *
10 0.69984795 *
14 1.88790230 6*
4 1.42034552 ***
17 2.45191084 6*Looking at statement evaluations
Filtering statements, based one:
ConsensusDistinguishes allDistinguishes f1Distinguishes f1 onlyetc.
dist.and.cons f1_f2 sig_f1_f2 f1_f3 sig_f1_f3 f2_f3
5 Consensus -0.056957901 0.50691520 0.56387310
6 Consensus 0.003736641 0.32043042 0.31669378
21 Consensus -0.421235496 -0.10463314 0.31660236
30 Consensus 0.451444563 0.51047007 0.05902551
35 Consensus -0.158946942 0.03653227 0.19547921
sig_f2_f3
5
6
21
30
35 References
Zabala, Aiora. 2014. “Qmethod: A Package to Explore Human Perspectives Using q Methodology.”
Using the qmethod package in R for analysis