R package for the analysis of the User Experience Questionnaire (UEQ)
Analyses items and scales of the the User Experience Questionnaire as described by Hinderks, Schrepp and Thomaschewski on https://www.ueq-online.org.
clean_ueq()
removes inconsistent data entries with the method described by the questionnaire's authors: If the difference between the best and worst evaluation of an item differs by more than 3 points on the scale this is seen as an indicator for a problematic data pattern. Rows where this is the case for 3 or more scales will be removed.ueq_items()
andueq_scales()
both return a data frame with the analysed items and scales (including descriptive statistics, confidence intervals and benchmarks).ueq_case_scale_means()
calculates means for each scale per case. Can be used to further analyse differences in mean values.analyze_ueq()
takes an additional column by which the data will be grouped in order to perform a comparison of means.
devtools::install_github("gitc23/ueqr")
Functions work out of the box with just a dataframe supplied. Column names MUST conform to the order of items in the original questionnaire.
ueq_scales(sample_items, is.clean = FALSE, ueq_range = c(1:26))
scale means vars sd n confidence ci_left ci_right alpha lambda2 benchmark
1 attractiveness 1.5077031 1.2609578 1.1229238 238 0.1426625 1.3650406 1.6503656 0.9144399 0.9097830 Above Average
2 perspicuity 1.7121849 1.2148087 1.1021836 238 0.1400276 1.5721573 1.8522124 0.8345766 0.7958659 Good
3 efficiency 1.6344538 1.1184493 1.0575676 238 0.1343593 1.5000945 1.7688131 0.8248765 0.7948633 Good
4 dependability 1.2731092 0.7689785 0.8769142 238 0.1114081 1.1617012 1.3845173 0.5990847 0.6082850 Above Average
5 stimulation 1.1838235 1.1161191 1.0564654 238 0.1342193 1.0496043 1.3180428 0.8272948 0.8071646 Above Average
6 novelty 0.8266807 1.1140847 1.0555021 238 0.1340969 0.6925838 0.9607776 0.7155509 0.6715016 Above Average
analyze_ueq(sample_items, is.clean = FALSE, ueq_range = c(1:26), group_var = 27)
# A tibble: 18 x 8
.y. group1 group2 p p.adj p.format p.signif method
<fct> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 attractiveness 2 3 0.701 1 0.70 ns Wilcoxon
2 attractiveness 2 1 0.952 1 0.95 ns Wilcoxon
3 attractiveness 3 1 0.708 1 0.71 ns Wilcoxon
4 perspicuity 2 3 0.490 1 0.49 ns Wilcoxon
5 perspicuity 2 1 0.978 1 0.98 ns Wilcoxon
6 perspicuity 3 1 0.414 1 0.41 ns Wilcoxon
7 efficiency 2 3 0.300 0.9 0.30 ns Wilcoxon
8 efficiency 2 1 0.634 1 0.63 ns Wilcoxon
9 efficiency 3 1 0.561 1 0.56 ns Wilcoxon
10 dependability 2 3 0.538 1 0.54 ns Wilcoxon
11 dependability 2 1 0.699 1 0.70 ns Wilcoxon
12 dependability 3 1 0.887 1 0.89 ns Wilcoxon
13 stimulation 2 3 0.143 0.38 0.14 ns Wilcoxon
14 stimulation 2 1 0.909 0.91 0.91 ns Wilcoxon
15 stimulation 3 1 0.126 0.38 0.13 ns Wilcoxon
16 novelty 2 3 0.241 0.72 0.24 ns Wilcoxon
17 novelty 2 1 0.916 0.92 0.92 ns Wilcoxon
18 novelty 3 1 0.246 0.72 0.25 ns Wilcoxon