PSYC FPX 3700 Assessment 2

PSYC FPX 3700 Assessment 2

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Name

Capella University

PSYC-FPX3700 Statistics for Psychology

Prof. Name

Date

Part 1: Data Visualization

Dataset Overview

The dataset GSS_30s.csv was derived from the General Social Survey (GSS), specifically focusing on participants aged 30 to 39 who completed the survey in 2022. The GSS is a well-established national survey that collects demographic, behavioral, and attitudinal data from adults in the United States. The purpose of this analysis is to visualize and interpret selected variables to better understand patterns related to mental health within this age group.

The dataset includes the following variables:

This dataset provides a foundation for exploring both univariate and bivariate relationships among variables, particularly focusing on mental health indicators.

A) Univariate Graph

Criteria

• Construct a univariate graph in JASP.
• Interpret the graph in simple terms for a non-statistical audience.

A histogram of the variable mntlhlth (number of days with poor mental health in the past 30 days) was generated in JASP.

Interpretation

The histogram indicates that the majority of participants reported experiencing few or no poor mental health days during the past month. Most responses clustered toward the lower end of the scale, forming a right-skewed distribution. This suggests that while a smaller portion of participants experienced frequent poor mental health days, the majority of individuals in the 30–39 age range generally reported good mental well-being. The skewness implies that occasional or minimal distress is more common than prolonged or severe mental health difficulties within this age demographic.

In simpler terms, most individuals in their thirties tend to have good mental health overall, with only a few experiencing ongoing mental challenges throughout the month.

B) Bivariate Graph

Criteria

• Construct a bivariate graph in JASP.
• Interpret the graph for an audience with an advanced background in statistics.

A raincloud plot was created to compare the number of poor mental health days (mntlhlth) between two groups: participants who reported being diagnosed with depression (depress = Yes) and those who had not (depress = No).

Interpretation

The raincloud plot revealed a clear distinction between the two groups. Participants who reported a prior diagnosis of depression demonstrated a higher mean and greater variability in poor mental health days compared to those without such a diagnosis. The non-depressed group’s data were concentrated near zero, with relatively low dispersion, indicating minimal mental distress. Conversely, the depression group’s distribution exhibited higher central tendency and broader spread, suggesting more frequent and variable experiences of poor mental health.

Statistically, this pattern indicates a strong association between depression diagnosis and increased mental health difficulties. The overlap between the two distributions highlights that while some individuals without a depression diagnosis still experience poor mental health, on average, diagnosed individuals report significantly more mental health challenges.

Part 2: Sampling Distribution and Confidence Intervals

Dataset Overview

The second dataset, Assessment_2_Data.csv, is a hypothetical dataset simulating a simple random sample of Capella University undergraduate learners enrolled in a psychology program. These data allow for inferential analysis using sampling distributions and confidence intervals to generalize findings to a broader population of psychology learners.

The dataset includes the following variables:

Criteria

• Construct a graph in JASP.
• Compute descriptive statistics.
• Construct a confidence interval.
• Identify the generalizable population.

Descriptive Statistics and Graphical Representation

A histogram was generated to display the distribution of participant ages. Descriptive statistics (mean, median, standard deviation, and range) were calculated using JASP to summarize the dataset. These descriptive measures provide insight into the central tendency and variability of participants’ ages.

For example, if the sample’s mean age is 33.8 years with a standard deviation of 5.1, this indicates that most students fall within a relatively narrow age range, typically in their early to mid-thirties. The graphical representation confirms a near-normal distribution, supporting the use of parametric analyses for inferential testing.

Confidence Interval Construction

A 95% confidence interval for the mean age was constructed using JASP. This interval provides a range of plausible values within which the true population mean age likely falls. For instance, if the computed interval is between 32.9 and 34.7 years, we can be 95% confident that the average age of all psychology undergraduates at Capella University lies within this range.

Population Generalization

Because the dataset represents a simple random sample, the calculated confidence interval can reasonably be generalized to all undergraduate psychology students enrolled at Capella University. This inference assumes random sampling integrity and that the sample is sufficiently large to approximate the normal distribution of the population mean.

References

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association.

Capella University. (2025). PSYC-FPX3700: Research methods and statistics in psychology. Capella University Learning Portal.

PSYC FPX 3700 Assessment 2

National Opinion Research Center. (2022). General Social Survey (GSS) 2022. NORC at the University of Chicago. Retrieved from https://gss.norc.org

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