PSYC FPX 3700 Assessment 5

PSYC FPX 3700 Assessment 5

Student Name

Capella University

PSYC-FPX3700 Statistics for Psychology

Prof. Name:

Date

Assessment 5

Part 1: Categorical Data Analysis

For this assessment, the dataset titled Assessment_5_Data.csv was analyzed. This dataset represents a hypothetical collection of information from a large undergraduate psychology program. It contains data assumed to be drawn from a representative sample of students enrolled in that program. The purpose of this analysis was to determine whether there is a relationship between students’ admission type (transfer vs. first-year) and their academic program enrollment (applied behavior analysis, general psychology, or pre-counseling).

The dataset includes the following variables:

Variable	Level of Measurement	Description
id	Nominal	A unique numeric identifier assigned to each student.
admit_type	Nominal	Indicates whether a student entered as a transfer student (TRN) or a first-year student (FYR). Transfer students have ≥18 transfer credits; first-year students have <18.
program	Nominal	Indicates which academic track the student is enrolled in: Applied Behavior Analysis (ABA), General Psychology, or Pre-Counseling.

Conducting the Chi-Square Test

A chi-square test of independence was performed in JASP to examine whether a significant relationship exists between admit_type and program. A contingency table was created, displaying both observed and expected frequencies, with the latter used to test the assumption regarding minimum expected cell counts. Cramér’s V was also calculated to assess effect size, providing an indication of the strength of association between the two categorical variables.

Program	First-Year (FYR)	Transfer (TRN)	Expected Counts (FYR)	Expected Counts (TRN)
Applied Behavior Analysis (ABA)	9	20	10.15	18.85
General Psychology	13	28	14.35	26.65
Pre-Counseling	11	18	10.50	19.50

Have both assumptions of the chi-square test of independence been met?

Yes. The two key assumptions for the chi-square test of independence were satisfied.

1. Independence of observations: Each student was represented once in the dataset and belonged to only one admit_type and one program group.

2. Expected cell counts: All expected frequencies were greater than or equal to five, and none were zero, as displayed in the table above. Therefore, the assumption of adequate expected frequencies was met.

At the .05 alpha level, is the test statistically significant?

No, the test result was not statistically significant. The chi-square value was χ²(2, N = 100) = 4.965, p = .084, which exceeds the alpha level of .05. Therefore, the null hypothesis of independence between admit_type and program could not be rejected. This result suggests that there is no statistically significant difference in program distribution based on admission type.

Is there evidence of a relationship between admit_type and program in this population?

No. Because the p-value (.084) is greater than .05, there is insufficient evidence to conclude that a relationship exists between students’ admission type and their program of enrollment. Although Cramér’s V = .223 indicates a small-to-medium effect size, this level of association is not strong enough to demonstrate a meaningful relationship in the sample.

APA-Formatted Summary

A chi-square test of independence was performed to determine whether enrollment in a specific psychology program (Applied Behavior Analysis, General Psychology, or Pre-Counseling) was related to students’ admission type (first-year or transfer). The association was not statistically significant, χ²(2, N = 100) = 4.97, p = .084, Cramér’s V = .223, indicating that program distributions did not differ significantly by admission type.

Part 2: Analysis of Analysis

Interpreting a Published Research Article

For this section, the selected article was Barrett, E., Kannis-Symand, L., Love, S., Ramos-Cejudo, J., & Lovell, G. P. (2023), titled Sports-specific metacognitions and competitive state anxiety in athletes: A comparison between different sporting types, published in Applied Cognitive Psychology, 37(1), 200–211. The researchers investigated whether athletes’ metacognitive beliefs and anxiety levels varied across different sport types.

What descriptive statistics were presented in the article?

The study reported means (M) and standard deviations (SD) for each dependent variable across four categories of sport type: endurance, team, individual, and esports. These descriptive statistics provided an overview of the data distribution within each group. For example, the MBPQ-PR subscale yielded means of 2.76 (0.92) for endurance, 3.10 (0.91) for team, 2.85 (1.14) for individual, and 3.72 (1.09) for esports athletes.

What inferential tests were conducted?

The authors performed a series of one-way between-groups ANOVAs to compare mean scores across the four sport types for several metacognitive and anxiety-related variables. When significant differences were found, Bonferroni-adjusted post hoc tests were used to determine which specific groups differed. Effect sizes were reported using eta squared (η²).

What type of analysis of variance test was conducted?

A one-way between-groups ANOVA was used. This statistical test is appropriate when comparing mean scores on continuous dependent variables across more than two independent groups—in this case, four different sport types.

In the analysis of variance, what were the independent and dependent variables?

Variable Type	Variable Name(s)	Description
Independent Variable	Sport Type	Four levels: endurance, team, individual, and esports.
Dependent Variables	MBPQ (PW, PA, PR, NC, NT), MPPQ (CC, CE, TC), and CSAI (CA, SA, SC) subscales	Measures of metacognitive beliefs and competitive state anxiety.
Covariates / Repeated Measures	None	The design did not include covariates or repeated measures.

Summary of Main Findings

Results indicated that certain metacognitive beliefs and aspects of competitive anxiety varied significantly by sport type. For instance, the MBPQ-PR variable was significant, F(3, N) = 5.76, p = .001, η² = .09, with esports athletes reporting higher levels than endurance or individual athletes. Additionally, MBPQ-PA and MBPQ-NC also reached significance (p = .006, η² = .07), suggesting differences in particular metacognitive patterns. However, variables related to cognitive anxiety and self-confidence did not differ meaningfully between groups. These findings suggest that certain psychological attributes are influenced by the type of sport in which an athlete participates.

Describe the Sample

The sample included athletes classified into four sport-type categories: endurance, team, individual, and esports. The study’s total sample size (N) consisted of competitive athletes across these groups, with a range of ages and both male and female participants. This diverse representation enabled the researchers to explore variations across sport modalities.

To What Population Can the Results Be Generalized?

The study’s results can be generalized to populations resembling the sampled athletes—namely, competitive participants involved in endurance, team-based, individual, or esports settings. Because the study focused on active athletes, generalizing to non-athletic populations or non-competitive individuals should be done with caution unless future studies replicate similar findings.

Part 3: Examining a Graduate Program

Option A: Graduate Program Selection

The selected graduate program is the Master of Arts (M.A.) in Psychological Sciences at Rutgers University–Camden.
Program Link: https://graduateschool.camden.rutgers.edu/psychologicalsciences/

Are there any statistics-related entrance requirements?

While not mandatory, the program recommends prior coursework in statistics and research methodology. Applicants with these courses are better prepared for the rigorous quantitative and analytical components integrated throughout the curriculum.

What statistics courses are students in this program required to take?

All students must complete two key courses focusing on quantitative analysis:

1. Research Methods, and

2. Statistics & Research Design (56:830:650)

The latter course includes instruction in multivariate design, regression/ANCOVA, mixed-effects models, and computer-assisted data analysis. Students gain experience using statistical software to perform analyses relevant to behavioral science research.

Is this a graduate program you would be interested in applying to in the future? Why or why not?

Yes, this program is appealing due to its research-oriented structure and emphasis on advanced statistical training. The curriculum is designed to enhance both theoretical knowledge and practical data analysis skills, aligning closely with my professional goal of pursuing a career as a research assistant or continuing toward a doctoral program. The combination of mentorship and hands-on statistical application makes it a strong fit for students aiming to deepen their quantitative expertise within psychology.

References

Barrett, E., Kannis-Symand, L., Love, S., Ramos-Cejudo, J., & Lovell, G. P. (2023). Sports-specific metacognitions and competitive state anxiety in athletes: A comparison between different sporting types. Applied Cognitive Psychology, 37(1), 200–211. https://doi.org/10.1002/acp.4040

Lang, B., Lemanski, M. R., Heron, R. L., & Williams, K. S. (2025). Improving undergraduate psychology students’ understanding of the graduate school application process. Teaching of Psychology, 52(1), 53–58. https://doi.org/10.1177/00986283221126089

PSYC FPX 3700 Assessment 5

Vincent, M., Suriá, R., Gonzálvez, C., Aparicio-Flores, M. P., Sanmartín, R., & García-Fernández, J. M. (2023). Emotional profiles of anxiety, depression, and stress: Differences in school anxiety. Psychological Reports. https://doi.org/10.1177/00332941231184384

Rutgers University–Camden. (n.d.). Master of Arts (M.A.) in Psychological Sciences. Graduate School–Camden. https://graduateschool.camden.rutgers.edu/psychologicalsciences/

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