Assessment 5
Part 1: Categorical Data Analysis
For this assessment you will use the Assessment_5_Data.csv file, which can be found on the
Assessment 5 page in Canvas. This is a hypothetical dataset. Assume these data were collected
from a representative sample of students from one large undergraduate-level psychology
program.
The dataset includes the following variables:
id: A unique nominal-level identification number assigned to each student
admit_type: Classification of whether the student was admitted as a transfer student
(trn) or first-year student (fyr). Transfer students are admitted with at least 18 transfer
credits while first-year students are admitted with less than 18 transfer credits.
program: Whether the student was enrolled in the applied behavior analysis (aba)
program, general psychology program, or pre-counseling program.
Criteria:
Conduct a chi-square test in JASP
Explain how to check the assumptions of a chi-square test
Interpret the statistical significance of a chi-square test
Write a paragraph in APA style summarizing the results of your analysis
In JASP, conduct a chi-square test of independence to determine if there is evidence of a
relationship between admit_type and program. Include the expected counts on your contingency
table. Request Cramér’s V as a measure of effect size.
1
, Have both assumptions of the chi-square test of independence been met? Explain how you checked
each assumption.
Yes. Independence was satisfied because each student appears once and belongs to one admit_type
and one program. Expected counts from the JASP table were all ≥ 5 and none were 0 (e.g., FYS–ABA =
10.15; TRN–ABA = 18.85; FYS–General = 14.35; TRN–General = 26.65; FYS–Pre-counseling = 10.50;
TRN–Pre-counseling = 19.50), so the chi-square assumption on expected counts was met.
At the .05 alpha level, is the test statistically significant? Explain how you determined this.
No. The chi-square test was not significant at α = .05: χ²(2, N = 100) = 4.965, p = .084 (> .05).
Therefore, we fail to reject the null hypothesis of independence.
Is there evidence of a relationship between admit_type and program in this population? Explain
how you determined this.
No. With p = .084, there is not sufficient evidence of a relationship between admit_type and program
in the population. Cramér’s V = .223 indicated a small–to–medium association, consistent with the
non-significant test.
Write a paragraph that briefly summarizes the study and reports the results of the chi-square test in
APA style. Most students will need between 40 and 60 words.
A chi-square test examined whether program enrollment (ABA, general, pre-counseling) differed by
admit type (first-year vs. transfer) among 100 students. The association was not significant, χ²(2, N =
100) = 4.97, p = .084, Cramér’s V = .223. Program distributions did not differ reliably by admit type.
2
Part 1: Categorical Data Analysis
For this assessment you will use the Assessment_5_Data.csv file, which can be found on the
Assessment 5 page in Canvas. This is a hypothetical dataset. Assume these data were collected
from a representative sample of students from one large undergraduate-level psychology
program.
The dataset includes the following variables:
id: A unique nominal-level identification number assigned to each student
admit_type: Classification of whether the student was admitted as a transfer student
(trn) or first-year student (fyr). Transfer students are admitted with at least 18 transfer
credits while first-year students are admitted with less than 18 transfer credits.
program: Whether the student was enrolled in the applied behavior analysis (aba)
program, general psychology program, or pre-counseling program.
Criteria:
Conduct a chi-square test in JASP
Explain how to check the assumptions of a chi-square test
Interpret the statistical significance of a chi-square test
Write a paragraph in APA style summarizing the results of your analysis
In JASP, conduct a chi-square test of independence to determine if there is evidence of a
relationship between admit_type and program. Include the expected counts on your contingency
table. Request Cramér’s V as a measure of effect size.
1
, Have both assumptions of the chi-square test of independence been met? Explain how you checked
each assumption.
Yes. Independence was satisfied because each student appears once and belongs to one admit_type
and one program. Expected counts from the JASP table were all ≥ 5 and none were 0 (e.g., FYS–ABA =
10.15; TRN–ABA = 18.85; FYS–General = 14.35; TRN–General = 26.65; FYS–Pre-counseling = 10.50;
TRN–Pre-counseling = 19.50), so the chi-square assumption on expected counts was met.
At the .05 alpha level, is the test statistically significant? Explain how you determined this.
No. The chi-square test was not significant at α = .05: χ²(2, N = 100) = 4.965, p = .084 (> .05).
Therefore, we fail to reject the null hypothesis of independence.
Is there evidence of a relationship between admit_type and program in this population? Explain
how you determined this.
No. With p = .084, there is not sufficient evidence of a relationship between admit_type and program
in the population. Cramér’s V = .223 indicated a small–to–medium association, consistent with the
non-significant test.
Write a paragraph that briefly summarizes the study and reports the results of the chi-square test in
APA style. Most students will need between 40 and 60 words.
A chi-square test examined whether program enrollment (ABA, general, pre-counseling) differed by
admit type (first-year vs. transfer) among 100 students. The association was not significant, χ²(2, N =
100) = 4.97, p = .084, Cramér’s V = .223. Program distributions did not differ reliably by admit type.
2
