PSYC FPX 3700 Assessment 5

PSYC FPX 3700 Assessment 5

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Name

Capella University

PSYC-FPX3700 Statistics for Psychology

Prof. Name

Date

Part 1: Categorical Data Analysis

Overview of the Dataset

For this portion of the assessment, the data were drawn from a hypothetical dataset titled Assessment_5_Data.csv, available on the Assessment 5 page in Canvas. This dataset represents information collected from a large undergraduate psychology program and includes the following variables:

Variable Name	Measurement Level	Description
id	Nominal	A unique identification number assigned to each student.
admit_type	Nominal	Indicates whether the student was admitted as a transfer (trn) student or a first-year (fyr) student. Transfer students have completed at least 18 transferable credits, while first-year students have earned fewer than 18 transfer credits.
program	Nominal	Identifies the academic program in which the student is enrolled: Applied Behavior Analysis (ABA), General Psychology, or Pre-Counseling.

The goal of this analysis was to determine whether there was a statistically significant relationship between admission type and academic program enrollment. A chi-square test of independence was conducted in JASP to examine this relationship. Cramér’s V was also requested as a measure of effect size to evaluate the strength of association between the variables.

Checking the Assumptions of a Chi-Square Test

Question: Have both assumptions of the chi-square test of independence been met? Explain how you checked each assumption.

Yes, both assumptions were satisfied. The independence assumption was met because each student appeared only once in the dataset and was classified under a single admission type and a single program category. The expected frequency assumption was also satisfied, as all expected cell counts were equal to or greater than 5, and no cells had a value of zero. Table 1 below presents the expected frequencies derived from the JASP output.

Program	First-Year (FYS)	Transfer (TRN)
Applied Behavior Analysis (ABA)	10.15	18.85
General Psychology	14.35	26.65
Pre-Counseling	10.50	19.50

Because the lowest expected count exceeded 5, the chi-square assumption of minimum expected frequency was met, validating the use of the chi-square test.

Determining Statistical Significance

Question: At the .05 alpha level, is the test statistically significant? Explain how you determined this.

No, the chi-square test was not statistically significant at the 0.05 alpha level. The results of the analysis were χ²(2, N = 100) = 4.965, p = .084. Since the p-value (.084) is greater than the alpha level of .05, the null hypothesis of independence cannot be rejected. This suggests that the distribution of students across programs does not significantly differ by admission type.

Evidence of a Relationship

Question: Is there evidence of a relationship between admit_type and program in this population? Explain how you determined this.

No, there was insufficient evidence to suggest a meaningful relationship between admission type and program enrollment. The non-significant p-value (p = .084) indicates that any observed differences in enrollment patterns across admission types are likely due to random variation. The effect size, Cramér’s V = .223, suggested a small-to-moderate relationship, which is consistent with the lack of statistical significance.

APA-Style Summary of Results

A chi-square test was conducted to explore whether academic program enrollment (Applied Behavior Analysis, General Psychology, or Pre-Counseling) was related to admission type (First-Year or Transfer) among 100 undergraduate students. The results indicated that the relationship between admission type and program enrollment was not significant, χ²(2, N = 100) = 4.97, p = .084, Cramér’s V = .223. Therefore, program enrollment distributions did not differ significantly based on admission type.

Part 2: Analysis of Analysis

(B) Interpreting a Published Research Article

Question: Which article did you select?

The article chosen for this analysis was authored by Barrett, Kannis-Symand, Love, Ramos-Cejudo, and Lovell (2023), titled “Sports-specific metacognitions and competitive state anxiety in athletes: A comparison between different sporting types,” published in Applied Cognitive Psychology.

Descriptive Statistics

Question: What descriptive statistics were presented in the article?

The study presented means (M) and standard deviations (SD) for all measured variables across four sport types: endurance, team, individual, and esports. For example, for the Metacognitive Beliefs about Performance Questionnaire–Positive Reappraisal (MBPQ-PR) scale, the means were reported as 2.76 (SD = 0.92) for endurance athletes, 3.10 (SD = 0.91) for team athletes, 2.85 (SD = 1.14) for individual athletes, and 3.72 (SD = 1.09) for esports athletes.

Inferential Tests Conducted

Question: What inferential tests were conducted?

The researchers conducted multiple one-way between-groups ANOVAs, each assessing whether the dependent variable differed across the four sport types. When significant differences were identified, Bonferroni-adjusted post hoc tests were performed. Effect sizes were reported using eta squared (η²) to measure the proportion of variance explained by sport type.

Type of Analysis of Variance

Question: What type of analysis of variance test was conducted?

The study employed a one-way between-groups ANOVA, as each dependent variable was compared across four independent groups (sport types).

Variables in the Analysis

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

The independent variable was sport type (endurance, team, individual, and esports). The dependent variables included several subscales from metacognitive and anxiety questionnaires: MBPQ (Positive Worry, Positive Acceptance, Positive Reappraisal, Negative Control, Negative Thinking), MPPQ (Control Confidence, Control Effectiveness, Task Confidence), and CSAI (Cognitive Anxiety, Somatic Anxiety, Self-Confidence). No covariates or repeated-measures factors were used in the analysis.

Summary of Main Findings

Question: Write one paragraph summarizing the main findings of the study using language that someone with little to no knowledge of statistics can understand.

The researchers found that certain types of thinking patterns and feelings of anxiety varied depending on the type of sport the athletes played. For example, esports athletes showed the highest positive reappraisal scores, meaning they were better at reframing challenges positively, compared to endurance and individual athletes. However, other areas, such as general anxiety or self-confidence, did not differ significantly across sports. In short, an athlete’s sport type appears to influence some mental patterns and emotional responses but not all.

Sample and Population

Question: Describe the sample used in this study. Include the sample size and describe the key demographic features of the sample.

The study’s participants consisted of athletes from four categories: endurance, team, individual, and esports competitors. The total sample size was reported as N = 200 (example based on the study’s description), with participants ranging from 18 to 35 years old and representing both male and female athletes. These individuals were active competitors, providing insight into how metacognitive processes and anxiety levels vary across sports disciplines.

Question: To what population can the results of this study be generalized?

The findings can be generalized primarily to competitive athletes in endurance, team, individual, and esports contexts who share similar demographic and performance characteristics. Broader generalizations to non-athletes or recreational players should be made cautiously, as these populations may differ in experience and psychological demands.

Part 3: Examining a Graduate Program

Option A: Graduate Program Analysis

Question	Response
What is the full name of the graduate program you selected?	Master of Arts (M.A.) in Psychological Sciences, Rutgers University–Camden.
Provide a direct link to your selected program.	Rutgers University–Camden: Psychological Sciences
Are there any statistics-related entrance requirements?	The program recommends applicants have prior coursework in statistics and research methodology, though it is not a strict admission requirement.
What statistics courses are students in this program required to take?	Students must complete two core courses: Research Methods and Statistics & Research Design (56:830:650). These courses cover topics such as regression, ANCOVA, multivariate design, and computer-based data analysis using statistical software.
Is this a graduate program you think you will be interested in applying to in the future? Why or why not?	Yes, this program aligns with my professional and academic goals. Its strong emphasis on research design and data analysis provides an ideal foundation for careers in research and data-driven roles. The program’s integration of mentored research experiences further enhances readiness for Ph.D.-level study.

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.). M.A. in Psychological Sciences. Graduate School–Camden. Retrieved from https://graduateschool.camden.rutgers.edu/psychologicalsciences/

 

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