Student Name Capella University PSYC-FPX3700 Statistics for Psychology Prof. Name Date PSYC FPX 3700 Assessment 5 Categorical Data Analysis & ANOVA PSYC FPX 3700 Assessment 5 focuses on applying statistical methods and interpreting psychological research. The assessment examines a chi-square test of independence using categorical student data, interprets a published study that uses one-way ANOVA, and evaluates the statistics-related requirements of a graduate psychology program. The chi-square analysis found no statistically significant association between admission type and program enrollment, χ²(2, N = 100) = 4.97, p = .084, while the reviewed research article found significant differences across sport types for several metacognitive measures. Part 1: Categorical Data Analysis The first part of PSYC FPX 3700 Assessment 5 uses the Assessment_5_Data.csv dataset. The hypothetical dataset represents undergraduate psychology students enrolled in a large academic program. Each observation corresponds to one student and includes an identification number, admission type, and academic program. The primary purpose of the analysis is to determine whether students’ admission status is associated with the psychology program in which they enroll. A chi-square test of independence is appropriate because both variables are categorical. Variables Included in the Dataset The dataset contains three primary variables: Conducting the Chi-Square Test of Independence A chi-square test of independence was conducted in JASP to determine whether admission type and program enrollment were statistically associated. This test evaluates whether the distribution of students among the three programs differs according to whether students entered as first-year or transfer students. The analysis also examined expected cell frequencies and calculated Cramér’s V. Expected frequencies help determine whether the assumptions of the chi-square test have been satisfied, while Cramér’s V provides an estimate of the strength of the relationship between the categorical variables. Assumptions of the Chi-Square Test Before interpreting a chi-square test, researchers should verify that the data meet the test’s assumptions. Two important considerations in this analysis were independence of observations and adequate expected cell frequencies. Independence of Observations Each student appears only once in the dataset and is classified into one admission category and one academic program. Therefore, each observation represents a separate student, satisfying the assumption that observations are independent. Expected Cell Frequencies The expected frequencies were also examined. All expected cell counts were at least 5, and none of the cells had an expected frequency of zero. Program First-Year Expected Count Transfer Expected Count Applied Behavior Analysis 10.15 18.85 General Psychology 14.35 26.65 Pre-Counseling 10.50 19.50 Because all expected frequencies meet the required threshold, the expected-count assumption for the chi-square analysis was satisfied. Statistical Significance of the Chi-Square Test The chi-square analysis was evaluated using an alpha level of .05. The results were: χ²(2, N = 100) = 4.97, p = .084. Because the p-value of .084 is greater than the .05 significance level, the result is not statistically significant. Therefore, the null hypothesis of independence cannot be rejected. In practical terms, the analysis does not provide sufficient statistical evidence to conclude that students’ admission type is related to their selected psychology program. Relationship Between Admission Type and Program Although the chi-square test was not statistically significant, the effect size was Cramér’s V = .223. Cramér’s V describes the strength of association between categorical variables, with larger values indicating stronger relationships. The observed effect suggests a modest association between admission type and program enrollment. However, because the chi-square test did not reach statistical significance, the finding should not be interpreted as evidence of a statistically reliable relationship in the population represented by the analysis. APA-Style Interpretation A chi-square test of independence was conducted to examine whether admission type was associated with program enrollment among 100 undergraduate psychology students. The analysis indicated that the association was not statistically significant, χ²(2, N = 100) = 4.97, p = .084, Cramér’s V = .223. Therefore, the distribution of students across Applied Behavior Analysis, General Psychology, and Pre-Counseling programs did not significantly differ according to whether students were first-year or transfer students. Part 2: Analysis of Variance The second part of the assessment involves interpreting a published psychological research article that used analysis of variance (ANOVA). The selected article is by Barrett et al. (2023), titled Sports Specific Metacognitions and Competitive State Anxiety in Athletes: A Comparison Between Different Sporting Types, published in Applied Cognitive Psychology. The researchers used ANOVA to investigate whether metacognitive beliefs and competitive anxiety differed among athletes participating in different types of sports. Descriptive Statistics in the Published Study The researchers reported means and standard deviations for the study’s outcome variables across four sport categories: For example, the MBPQ-PR (Metacognitive Belief in Personal Responsibility) subscale produced the following descriptive statistics: Sport Type Mean (M) Standard Deviation (SD) Endurance 2.76 0.92 Team 3.10 0.91 Individual 2.85 1.14 Esports 3.72 1.09 The mean represents the average score for each group, while the standard deviation describes the amount of variation in scores within that group. Descriptive statistics provide an initial picture of group differences before inferential statistical tests are interpreted. Type of ANOVA Used The researchers conducted one-way between-groups ANOVAs. The independent variable was sport type, which had four categories: endurance, team, individual, and esports. The dependent variables represented several dimensions of metacognitive beliefs, motivational processes, and competitive state anxiety. These included: The analysis did not include covariates or repeated-measures factors. Inferential Statistical Analysis One-way ANOVA is useful when researchers want to compare the means of three or more independent groups. In this study, the researchers used separate ANOVAs to determine whether athletes from different sport types differed on the measured psychological variables. When a statistically significant overall effect was identified, Bonferroni-adjusted post hoc comparisons were used to determine which specific groups differed from one another. The researchers also reported eta squared (η²) as an effect-size measure. Summary of the ANOVA Findings Several metacognitive and anxiety-related measures differed significantly according to sport type. For example, the MBPQ-PR measure produced a statistically significant result: F(3, N) = 5.76, p = .001, η² = .09. The results indicated that esports athletes had the highest scores on

Student Name Capella University PSYC-FPX3700 Statistics for Psychology Prof. Name Date PSYC FPX 3700 Assessment 4: Correlation and Linear Regression PSYC FPX 3700 Assessment 4 focuses on two important statistical techniques: Pearson correlation and simple linear regression. In Part 1, the relationship between an established test anxiety measure and a newly developed test anxiety measure is examined using Pearson’s correlation coefficient. In Part 2, linear regression is used to determine whether students’ self-efficacy in data visualization predicts their quiz performance. The findings indicate a strong positive relationship between the two test anxiety measures and show that self-efficacy is a statistically significant predictor of quiz scores. Part 1: Correlation for Relationships Overview of the Study The first part of the assessment uses the Assessment 4a Data.csv dataset from the Assessment 4 module in Canvas. The dataset represents a hypothetical but realistic sample of students enrolled in an introductory statistics course. Before completing their first examination, students responded to two measures of test anxiety: Old Test Anxiety and New Test Anxiety. The older measure has been established as a reliable and validated instrument. However, researchers identified concerns that some of its items might not adequately reflect the experiences of contemporary students. A new test anxiety measure, New_Test_Anxiety, was therefore developed. The purpose of the analysis is to determine how closely the new measure corresponds with the established instrument and whether the results provide evidence supporting its validity. Variables in the Dataset The dataset contains several variables used to describe the students and analyze their test anxiety scores. Variable Measurement Level Description Student_ID Nominal Unique identification number for each participant Primary_Degree Categorical Student’s primary degree, such as BA, BS, or BSN GPA Continuous Grade point average at the beginning of the term Old_Test_Anxiety Continuous Score obtained from the established test anxiety scale New_Test_Anxiety Continuous Score obtained from the newly developed test anxiety scale Creating Visualizations in JASP JASP was used to create scatterplots and histograms for Old_Test_Anxiety and New_Test_Anxiety. Visual inspection is important before calculating Pearson’s correlation because it helps determine whether the data meet the assumptions of the statistical test. The scatterplot showed a strong, positive, and approximately linear relationship between the two test anxiety measures. In other words, students who scored higher on the established measure generally also scored higher on the new measure. The histograms suggested that both variables were approximately normally distributed. There were also no extreme outliers that appeared likely to substantially affect the correlation analysis. Why Pearson’s r Is Appropriate Pearson’s correlation coefficient, commonly represented as r, is appropriate when researchers want to examine the strength and direction of a linear relationship between two continuous variables. In this analysis, both Old_Test_Anxiety and New_Test_Anxiety are continuous variables. The scatterplot also demonstrated an approximately linear relationship, while the distributions appeared reasonably normal and did not contain problematic extreme outliers. Therefore, Pearson’s r is an appropriate statistical method for examining the relationship between the two test anxiety measures. Pearson Correlation Results The JASP correlation analysis produced the following result: r = .919, 95% CI [.863, .952], n = 54, p < .001 This result indicates a very strong positive correlation between Old_Test_Anxiety and New_Test_Anxiety. The correlation coefficient of .919 means that students’ scores on the two measures tend to move together in a strong linear pattern. The confidence interval, ranging from .863 to .952, also indicates that the population correlation is likely to be strongly positive. Is There Evidence of a Relationship in the Population? Yes. The results provide strong statistical evidence of a relationship between Old_Test_Anxiety and New_Test_Anxiety in the population represented by the sample. The p value was less than .001, which means the observed correlation would be very unlikely if there were no relationship between the two variables in the population. Because the correlation is both statistically significant and large in magnitude, the findings provide strong evidence of a positive association between the two test anxiety measures. However, correlation alone does not establish causation. The result indicates that the two measures are strongly associated, not that one causes changes in the other. Reliability and Validity Implications The strong positive correlation provides evidence for convergent validity, which is one form of construct validity. Convergent validity is supported when different instruments intended to measure the same or closely related construct produce similar results. Because both Old_Test_Anxiety and New_Test_Anxiety are designed to measure test anxiety, their strong association suggests that the new instrument is measuring a construct that is consistent with the established instrument. PSYC FPX 3700 Assessment 4 Correlation & Regression Analysis This finding can therefore contribute to evidence supporting the validity of the New_Test_Anxiety scale. It is important to distinguish this finding from reliability. A high correlation between two instruments primarily provides evidence about their relationship and construct validity. Additional reliability analyses, such as internal consistency or test-retest reliability, would be needed to evaluate the reliability of the new scale more directly. Part 2: Simple Linear Regression Overview of the Study The second portion of PSYC FPX 3700 Assessment 4 uses the Assessment_4b_Data.csv dataset. The data represent a hypothetical group of students enrolled in a large introductory statistics course. Before completing a quiz on data visualization, students completed a survey measuring their self-efficacy in data visualization skills. The purpose of the analysis is to determine whether students’ self-efficacy can statistically predict their performance on the data visualization quiz. Simple linear regression is appropriate because the analysis examines whether one continuous predictor variable, self-efficacy, can explain or predict variation in one continuous outcome variable, quiz performance. Variables Used in the Regression Analysis Variable Measurement Level Description id Nominal Unique identification number for each student quiz_score Continuous Score representing performance on the data visualization quiz self_efficacy Continuous Composite score representing confidence in data visualization skills Creating Regression Visualizations in JASP Several graphs were created in JASP to evaluate the regression model and its assumptions. The scatterplot displayed self_efficacy on the x-axis and quiz_score on the y-axis. This visualization helped assess whether the relationship between the predictor and outcome appeared

Student Name Capella University PSYC-FPX3700 Statistics for Psychology Prof. Name Date PSYC FPX 3700 Assessment 3 Hypothesis Testing & Data Analysis PSYC FPX 3700 Assessment 3 focuses on two important statistical procedures: a binomial test and a Welch’s independent-samples t-test. In Part 1, the binomial test indicates that women represent significantly more than 50% of the grocery store customer sample. In Part 2, the Welch’s t-test shows that transfer students are significantly older, on average, than first-year students. Together, these analyses demonstrate how hypothesis testing can be used to answer practical questions using sample data. Assessment 3 Part 1: Introduction to Hypothesis Testing The first part of the assessment uses the Assessment_3a_Data.csv dataset, which contains hypothetical information collected from a random sample of grocery store customers. The analysis is conducted in JASP and focuses on determining whether more than half of the customers identify as women. Hypothesis testing provides a structured way to determine whether a pattern observed in a sample provides enough statistical evidence to support a claim about a larger population. Understanding the Grocery Store Dataset The dataset contains information about customers and their shopping behavior. The primary variables include: For this analysis, the primary variable of interest is Gender because the research question concerns the proportion of customers who identify as women. Research Question for the Binomial Test The grocery store’s marketing team wants to determine whether women account for more than half of its customers. The research question is: Are more than half of all the store’s customers women? Because the question concerns whether a population proportion is greater than 50%, a one-sample binomial test is appropriate for evaluating the observed proportion. Null and Alternative Hypotheses The hypotheses establish what the statistical test is designed to evaluate. Null hypothesis (H₀): The proportion of female customers is equal to 50%. H₀: p = 0.50 Alternative hypothesis (H₁): The proportion of female customers is greater than 50%. H₁: p > 0.50 The null hypothesis represents the assumption that women account for exactly half of the customer population. The alternative hypothesis represents the possibility that women make up more than half. Conducting the Binomial Test in JASP A binomial test was conducted in JASP using the grocery store customer data. The sample included 97 customers, of whom 61 identified as women. This means that women represented approximately 62.9% of the sample. The resulting p-value was .014. Because this value is below the conventional significance level of α = .05, the result is statistically significant. Decision About the Null Hypothesis The statistical decision is to reject the null hypothesis. The p-value of .014 indicates that, assuming the true population proportion were 50%, obtaining a result this extreme would be relatively unlikely. Therefore, the sample provides sufficient statistical evidence to support the conclusion that more than half of the store’s customers are women. Interpretation for a General Audience The results indicate that approximately 63% of the customers in the sample identified as women. The statistical test suggests that this proportion is significantly greater than 50%. For the grocery store, this finding may provide useful information for marketing and customer engagement strategies. For example, the business could examine purchasing patterns, loyalty-program participation, and product preferences to determine whether its marketing efforts should be adjusted based on the characteristics of its customer base. It is important to distinguish statistical significance from business impact. Although the result is statistically significant, additional information about purchasing behavior would be needed before making specific marketing decisions. Assessment 3 Part 2: Comparing the Mean Ages of Two Groups The second part of PSYC FPX 3700 Assessment 3 uses the Assessment_3b_Data.csv dataset. This dataset contains information about students recently admitted to bachelor’s degree programs at a large online university. The objective is to determine whether first-year (FYR) students and transfer (TRN) students differ significantly in their mean ages. Understanding the Student Dataset The dataset includes several variables relevant to student characteristics and admission status: The primary variables used in this analysis are Admit_Status and Age. Research Question for the t-Test The research question is: Do first-year students and transfer students differ in their mean ages? This question requires comparing the average age of two independent groups. A statistical test is needed to determine whether the observed difference is large enough to be considered statistically significant rather than simply the result of sampling variation. Why Welch’s t-Test Was Used A Welch’s independent-samples t-test was selected because it compares the means of two independent groups without requiring the assumption that the groups have equal population variances. Welch’s t-test is particularly useful when the groups differ in sample size or variability. In this dataset, the first-year and transfer groups have different sample sizes and noticeably different standard deviations. Therefore, Welch’s approach provides a more appropriate and robust comparison than a traditional Student’s independent-samples t-test when equal variances cannot reasonably be assumed. Null and Alternative Hypotheses The hypotheses for the comparison are: Null hypothesis (H₀): The mean ages of first-year and transfer students are equal. H₀: μ_FYR = μ_TRN Alternative hypothesis (H₁): The mean ages of first-year and transfer students are different. H₁: μ_FYR ≠ μ_TRN Because the alternative hypothesis predicts a difference in either direction, this is a two-tailed test. Results of the Welch’s t-Test The Welch’s t-test produced the following results: Student Group n Mean Age (M) Standard Deviation (SD) First-Year (FYR) 86 27.30 4.15 Transfer (TRN) 69 32.29 7.08 The analysis produced a statistically significant result, Welch’s t(104.4) = −5.18, p < .001. The estimated effect size was Cohen’s d = −0.86, which represents a large effect. The 95% confidence interval for the mean difference was [−6.90, −3.08]. Interpretation of the t-Test Results The results indicate that first-year and transfer students differ significantly in their average ages. First-year students had a mean age of 27.30 years, whereas transfer students had a mean age of 32.29 years. In practical terms, transfer students in this sample were approximately five years older on average than first-year students. The large effect size indicates

Student Name Capella University PSYC-FPX3700 Statistics for Psychology Prof. Name Date PSYC FPX 3700 Assessment 2: PSYC FPX 3700 Assessment 2 focuses on using descriptive statistics, data visualization, sampling distributions, and confidence intervals to interpret psychological and demographic data. The assessment examines two datasets: one based on General Social Survey (GSS) participants ages 30–39 and another representing a simple random sample of Capella University undergraduate psychology learners. The analysis uses JASP to create graphs, summarize variables, compare groups, and interpret a 95% confidence interval for participants’ ages. Overall, the results illustrate how statistical methods can help researchers identify patterns in mental health and make reasonable conclusions about a larger population. Part 1: Data Visualization Dataset Overview For Part 1, the dataset GSS_30s.csv, available through the Week 3 Assessment page in Canvas, was analyzed. The data originate from the General Social Survey (GSS) and were restricted to respondents between 30 and 39 years of age who participated in the 2022 survey. The General Social Survey provides information about a broad range of social, demographic, and behavioral characteristics. In this assessment, the selected variables provide an opportunity to examine participants’ demographic characteristics and self-reported mental health experiences. The primary variables included in the dataset are summarized below: Variable Description year Year in which the participant’s data were collected id_ Unique identification number assigned to each participant childs Number of children reported by the participant age Participant’s age in years sex Sex assigned at birth race Self-reported racial category income Annual income category or range mntlhlth Number of days of poor mental health during the previous 30 days depress Whether a professional has ever diagnosed the participant with depression Univariate Graph: Mental Health Days Graph Construction A histogram was created in JASP using the mntlhlth variable. A histogram is useful for examining the distribution of a single quantitative variable because it shows how frequently different values occur within the dataset. In this case, the histogram illustrates how many days participants reported experiencing poor mental health during the previous 30 days. Interpretation for a General Audience The histogram indicates that most adults in the 30–39 age group reported relatively few days of poor mental health during the previous month. The distribution is right-skewed, meaning that most observations are concentrated toward the lower end of the scale while a smaller group reported substantially more days of poor mental health. This pattern suggests that mental health experiences vary considerably among adults in this age group. Although many participants reported relatively few poor mental health days, a smaller portion experienced mental health difficulties more frequently. The distribution should not be interpreted as proving that most adults in this age group have good mental health. Instead, it describes the pattern observed in this particular sample. Self-reported mental health measures can also be influenced by individual perceptions, circumstances, and reporting practices. Bivariate Graph: Depression Diagnosis and Mental Health Days Graph Construction A raincloud plot was created in JASP to examine the relationship between depression diagnosis and the number of poor mental health days. In this analysis, depress served as the grouping variable, while mntlhlth was the dependent variable. Raincloud plots combine features such as distributions and individual observations, making them useful for comparing the center, spread, and overall shape of data across groups. Interpretation for an Advanced Audience The raincloud plot shows a noticeable difference between participants who reported a professional diagnosis of depression and those who did not. Participants with a reported depression diagnosis generally showed a higher number of poor mental health days and greater variability in their responses. In comparison, participants without a reported depression diagnosis tended to cluster toward fewer poor mental health days. There is still some overlap between the groups, demonstrating that not every participant with a depression diagnosis experienced the same level of mental health difficulty and that individuals without a diagnosis could also report poor mental health. The observed pattern indicates an association between depression diagnosis and the number of reported poor mental health days. However, the visualization alone does not establish a causal relationship. For example, the analysis cannot demonstrate that depression directly caused participants to experience more poor mental health days. Part 2: Sampling Distribution and Confidence Intervals Dataset Description Part 2 uses a hypothetical dataset called Assessment_2_Data.csv, available through the Assessment 2 Canvas page. The dataset represents a simple random sample of Capella University undergraduate psychology learners. The dataset includes demographic information that can be used to conduct descriptive and inferential statistical analyses. The primary variables include: Variable Description ID Unique identification number for each participant Age Participant’s age in years Gender_Identity Gender identity reported by the learner IPEDS_Race_Ethnicity Race and ethnicity category based on the IPEDS classification system Descriptive Statistics and Graphical Analysis A histogram of the Age variable was created in JASP to examine the distribution of participants’ ages. The distribution appears approximately bell-shaped, with many participants concentrated around their 30s and 40s and fewer participants at the lower and upper ends of the age range. The pattern indicates that the sample includes many adult learners. However, a sample’s age distribution should be interpreted based on the actual observations rather than assuming that it represents every student enrolled in the program. Confidence Interval for Age Descriptive statistics were calculated in JASP to determine the sample size, mean age, standard deviation, and 95% confidence interval. Statistic Result Sample size (N) 100 Mean age (M) 39.22 years Standard deviation (SD) 10.17 95% confidence interval 37.20–41.24 years The sample had 100 participants, with a mean age of 39.22 years and a standard deviation of 10.17 years. The 95% confidence interval extended from 37.20 to 41.24 years. Interpreting the 95% Confidence Interval The confidence interval provides a range of plausible values for the population mean based on the sample data and the statistical procedure used to construct the interval. A common but technically inaccurate interpretation is that there is a 95% probability that the fixed population mean falls within this particular interval. A more precise interpretation is that if

Student Name Capella University PSYC-FPX3700 Statistics for Psychology Prof. Name Date PSYC FPX 3700 Assessment 1: Introduction to Statistics in Psychology Statistics plays an important role in psychology because it helps researchers organize, analyze, and interpret information about human behavior. In PSYC FPX 3700 Assessment 1, students apply basic research-method concepts to General Social Survey (GSS) data, examine the relationship between race and reported poor mental health days, and use JASP to calculate descriptive statistics. The assessment also demonstrates how statistical knowledge can support careers and graduate study in psychology. Introduction to Research Methods Researchers use statistical methods to identify patterns, evaluate relationships, and draw evidence-based conclusions. One widely used source of social and behavioral data is the General Social Survey (GSS), a nationally representative survey of adults in the United States. The GSS includes information about demographics, attitudes, behaviors, and other characteristics that can be analyzed in psychological and sociological research. For this assessment, two variables are particularly important: race and mntlhlth. The race variable categorizes respondents as Black, White, or Other, while mntlhlth represents the number of days during the previous 30 days that respondents experienced poor mental health. The primary research question is whether the average number of poor mental health days differs across racial groups. Generalizing Findings From GSS Data If GSS data show that racial groups differ in their average number of reported poor mental health days, the findings can generally be extended to the adult population of the United States represented by the GSS sampling design. The GSS uses probability-based sampling methods designed to produce nationally representative information. Because the sample is intended to represent the broader U.S. adult population, researchers can use the results to make population-level inferences when the appropriate statistical assumptions are met. However, generalization should always consider the population represented by the specific dataset, sampling procedures, response rates, and limitations of the variables being analyzed. Can Race Be Considered the Cause of Differences in Mental Health? No. Finding that racial groups have different average numbers of poor mental health days does not demonstrate that race caused the differences. The GSS is an observational survey rather than a randomized experiment. Researchers do not assign participants to racial categories or manipulate race. As a result, the study can identify an association between race and reported mental health but cannot establish a direct causal relationship. Other factors may contribute to observed differences. These can include socioeconomic circumstances, access to healthcare, education, employment, discrimination, neighborhood conditions, social support, and other demographic or environmental factors. Therefore, researchers should avoid causal statements when interpreting these findings. Establishing causality generally requires stronger research designs and careful consideration of potential confounding variables and alternative explanations. How Race Categories Can Affect Study Validity The GSS race variable used in this analysis includes only three categories: Black, White, and Other. Although this approach may simplify statistical analysis, it can also create limitations. Broad racial categories may combine individuals with substantially different cultural, ethnic, geographic, and social experiences. For example, placing several populations into an “Other” category can make meaningful differences between those populations difficult to identify. This limitation may affect both the validity and interpretability of the findings. It can also reduce the ability to understand mental health patterns among specific racial and ethnic populations. Researchers should therefore acknowledge that the three-category race variable does not capture the full complexity of racial and ethnic identity in the United States. Statistics and Post-Graduation Opportunities in Psychology Statistical knowledge is valuable beyond individual psychology courses. Graduate programs and psychology-related employers frequently expect students and professionals to understand research methodology, data analysis, and statistical interpretation. For this assessment, students can explore either a graduate psychology program or a psychology-related job that requires statistical knowledge. Graduate Program: Quantitative Psychology One example is the Master of Science in Quantitative Psychology at the University of Illinois Urbana-Champaign. Quantitative psychology combines psychological research with statistical and mathematical methods. Applicants to quantitative psychology programs are typically expected to have a foundation in areas such as research methods and statistics. Additional preparation in mathematics, including calculus or linear algebra, may also be beneficial depending on the program. Students pursuing quantitative psychology may study advanced statistical methods, statistical modeling, multivariate analysis, psychometrics, and computational approaches to psychological research. This type of program demonstrates how statistical skills can become a central component of advanced psychology education. Psychology Career: Behavioral Health Data Analyst Another way to apply statistics is through a behavioral health data analyst position. Professionals in these roles use quantitative methods to examine behavioral and health-related information and communicate findings to researchers, clinicians, and other stakeholders. Common statistical skills may include working with software such as R, SPSS, or JASP, performing regression and analysis of variance (ANOVA), creating data visualizations, and interpreting statistical results. Typical responsibilities can include: These career requirements illustrate why students studying psychology benefit from developing practical statistical skills. Getting Started With JASP JASP is an open-source statistical analysis program that allows students and researchers to conduct a variety of statistical analyses through a user-friendly interface. It can be particularly helpful for students who are learning statistical concepts and want to analyze research datasets without relying entirely on programming. For this assessment, the dataset GSS_30s.csv contains a subset of General Social Survey data from respondents between 30 and 39 years of age who were surveyed in 2022. The dataset includes demographic, socioeconomic, and mental health variables. Understanding the Variables in the GSS Dataset The main variables used in the dataset can be summarized as follows: Variable Description Measurement Level year Year in which the data were collected Continuous id_ Unique respondent identification number Nominal childs Number of children Ratio age Respondent’s age in years Ratio sex Sex assigned at birth Nominal race Racial category Nominal income Annual income category Ordinal mntlhlth Number of days with poor mental health during the previous 30 days Ratio depress Whether the respondent reported a depression diagnosis Nominal Correctly identifying measurement levels is important because the type of variable determines which