Results

Descriptives

Descriptives
	TOTAL
N	72
Missing	0
Mean	12.5
Median	14.0
Mode	14.0ᵃ
Standard deviation	2.88
Minimum	4.00
Maximum	15.0
ᵃ More than one mode exists, only the first is reported

Plots

TOTAL

Reliability Analysis

Scale Reliability Statistics
	Cronbach's α
scale	0.736
[3]

Item Reliability Statistics
	If item dropped
	Cronbach's α
TOTAL	0.816
Q1_conv	0.732
Q2_conv	0.723
Q3_conv	0.729
Q4_conv	0.730
Q5_conv	0.729
Q6_conv	0.706
Q7_conv	0.720
Q8_conv	0.727
Q9_conv	0.726
Q10_conv	0.716
Q11_conv	0.725
Q12_conv	0.711
Q13_conv	0.720
Q14_conv	0.718
Q15_conv	0.718

Dichotomous Model

Instructions

Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
Use variable names with exactly one trailing number (e.g., i1, item2, age1); names without digits or with multiple numbers may not plot.
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.

Item Statistics
	Proportion	Measure	Infit	Outfit
Q1_conv	0.972	-4.691	0.905	0.217
Q2_conv	0.917	-3.335	0.832	0.506
Q3_conv	0.875	-2.758	1.148	1.657
Q4_conv	0.806	-2.049	1.326	1.427
Q5_conv	0.681	-1.101	1.359	1.536
Q6_conv	0.653	-0.919	0.841	0.851
Q7_conv	0.875	-2.758	0.902	0.555
Q8_conv	0.875	-2.758	1.092	1.337
Q9_conv	0.931	-3.578	0.940	0.422
Q10_conv	0.833	-2.308	0.889	0.734
Q11_conv	0.931	-3.578	0.802	0.454
Q12_conv	0.653	-0.919	0.952	1.026
Q13_conv	0.889	-2.932	0.894	0.450
Q14_conv	0.861	-2.597	0.895	0.644
Q15_conv	0.722	-1.390	1.056	1.167
Note. Infit= Information-weighted mean square statistic; Outfit= Outlier-sensitive means square statistic.
[4]

Model Fit

Model Fit
	Person Reliability
scale	0.487
[4]

Wright Map

[5]

Item Infit Plot

Item Outfit Plot

Distractor analysis

'breaks' are not unique

Instructions

Enter the correct answer separated by commas, but there must be no spaces between commas.
The distractor analysis uses a fixed 3-group structure(33%-34%-33%) to ensure stable and reliable results.
By a rule of thumb, all items with a discrimination lower than 0.2 (threshold in the plot), should be checked for content.
Feature requests and bug reports can be made on my GitHub.

Counts of respondents

Item Q1
	lower	middle	upper

Item Q2
	lower	middle	upper

Item Q3
	lower	middle	upper

Item Q4
	lower	middle	upper

Item Q5
	lower	middle	upper

Item Q6
	lower	middle	upper

Item Q7
	lower	middle	upper

Item Q8
	lower	middle	upper

Item Q9
	lower	middle	upper

Item Q10
	lower	middle	upper

Item Q11
	lower	middle	upper

Item Q12
	lower	middle	upper

Item Q13
	lower	middle	upper

Item Q14
	lower	middle	upper

Item Q15
	lower	middle	upper

[7]

Total summary

Item Q1

Item Q2

Item Q3

Item Q4

Item Q5

Item Q6

Item Q7

Item Q8

Item Q9

Item Q10

Item Q11

Item Q12

Item Q13

Item Q14

Item Q15

[7]

Distractor analysis

'breaks' are not unique

Instructions

Enter the correct answer separated by commas, but there must be no spaces between commas.
The distractor analysis uses a fixed 3-group structure(33%-34%-33%) to ensure stable and reliable results.
By a rule of thumb, all items with a discrimination lower than 0.2 (threshold in the plot), should be checked for content.
Feature requests and bug reports can be made on my GitHub.

Counts of respondents

Item Q1
	lower	middle	upper

Item Q2
	lower	middle	upper

Item Q3
	lower	middle	upper

Item Q4
	lower	middle	upper

Item Q5
	lower	middle	upper

Item Q6
	lower	middle	upper

Item Q7
	lower	middle	upper

Item Q8
	lower	middle	upper

Item Q9
	lower	middle	upper

Item Q10
	lower	middle	upper

Item Q11
	lower	middle	upper

Item Q12
	lower	middle	upper

Item Q13
	lower	middle	upper

Item Q14
	lower	middle	upper

Item Q15
	lower	middle	upper

[7]

Proportions of respondents

Item Q1
	Lower	Middle	Upper

Item Q2
	Lower	Middle	Upper

Item Q3
	Lower	Middle	Upper

Item Q4
	Lower	Middle	Upper

Item Q5
	Lower	Middle	Upper

Item Q6
	Lower	Middle	Upper

Item Q7
	Lower	Middle	Upper

Item Q8
	Lower	Middle	Upper

Item Q9
	Lower	Middle	Upper

Item Q10
	Lower	Middle	Upper

Item Q11
	Lower	Middle	Upper

Item Q12
	Lower	Middle	Upper

Item Q13
	Lower	Middle	Upper

Item Q14
	Lower	Middle	Upper

Item Q15
	Lower	Middle	Upper

[7]

Item difficulty and discrimination index
Item	Difficulty	ULI	RIT	RIR
Q1	.	.	.	.
Q2	.	.	.	.
Q3	.	.	.	.
Q4	.	.	.	.
Q5	.	.	.	.
Q6	.	.	.	.
Q7	.	.	.	.
Q8	.	.	.	.
Q9	.	.	.	.
Q10	.	.	.	.
Q11	.	.	.	.
Q12	.	.	.	.
Q13	.	.	.	.
Q14	.	.	.	.
Q15	.	.	.	.
Note. ULI:Upper-Lower Index based on 3 groups, RIT:Item-Total correlation, RIR: Item-Rest correlation. Missing values should be recoded as 0 or incorrect answer in jamovi.
[5]

Total summary

Item Q1

Item Q2

Item Q3

Item Q4

Item Q5

Item Q6

Item Q7

Item Q8

Item Q9

Item Q10

Item Q11

Item Q12

Item Q13

Item Q14

Item Q15

[7]

Histogram of total score

[5]

Discrimination plot

[5]

[6]

References

[1] The jamovi project (2025). jamovi. (Version 2.7) [Computer Software]. Retrieved from https://www.jamovi.org.

[2] R Core Team (2025). R: A Language and environment for statistical computing. (Version 4.5) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from CRAN snapshot 2025-05-25).

[3] Revelle, W. (2025). psych: Procedures for Psychological, Psychometric, and Personality Research. [R package]. Retrieved from https://cran.r-project.org/package=psych.

[4] Robitzsch,A., Kiefer, T., & Wu, M. (2020). TAM: Test Analysis Modules. (Version 4.1.4) [R package]. URL https://CRAN.R-project.org/package=TAM.

[5] Martinkova, P., & Drabinova, A. (2018). ShinyItemAnalysis: for teaching psychometrics and to enforce routine analysis of educational tests. (Version 1.4.2) [R package]. URL https://CRAN.R-project.org/package=ShinyItemAnalysis.

[6] Seol, H. (2025). snowIRT: Item Response Theory for jamovi. (Version 5.1.7) [jamovi module]. URL https://github.com/hyunsooseol/snowIRT.

[7] Willse, J. (2018). CTT: Classical Test Theory Functions. (Version 2.3.3) [R package]. URL https://CRAN.R-project.org/package=CTT.