Complete the following assignment by filling in all requested information. In this assignment, you will review mock studies and analyze data within each study. You will need to CAREFULLY follow the directions outlined in each section of the attached document using SPSS. Some of the studies require you to enter data and some require you to use the GSS data set. You will list the five steps of hypothesis testing for each Mock Study to see how every question should be formatted. You will decide whether to reject or fail to reject the null hypothesis based on the two-tailed p value. Be sure to cut and paste the appropriate SPSS outputs under each problem and interpret the outputs within the context of each mock study. Use a different (legible) color font for your responses.
NOTE: All calculations should be coming from your SPSS. Hand calculation IS not accepted. You are also required to submit the SPSS output file (*.spv) to get credit for this assignment. This .spv file should include ALL your outputs. In other words, continue to save your output file as you conduct each analysis.
Week 6 data
Explanation of the Topic and Research Question
The GSS 2018 dataset contains a variety of demographic, political, and social variables related to U.S. adults. In this context, we are interested in exploring whether there is a relationship between a specific behavior (such as purchasing stocks) and a subsequent change in attitudes or behaviors. Although the GSS dataset may not directly contain information about stock purchases by non-management personnel or stock price changes, we can still explore related behaviors, such as financial attitudes or investment patterns.
For the purposes of this assignment, let's define a modified research question based on available GSS 2018 data:
Research Question: Is there a positive correlation between the frequency of stock market investment (as reported by non-management personnel) and changes in their financial outlook (measured by their perceived financial situation)?
Step 1: State the Research and Null Hypotheses
We are now testing whether individuals who report higher frequencies of stock market investment experience a positive change in their perception of their financial situation.
· Research Hypothesis (H₁): There is a positive correlation between the frequency of stock market investment and the change in financial outlook.
· Null Hypothesis (H₀): There is no correlation between the frequency of stock market investment and the change in financial outlook.
Step 2: Specify the Alpha Level
We will use an alpha level of 0.05, which is standard in hypothesis testing. If the p-value from the test is less than 0.05, we will reject the null hypothesis and conclude that there is a statistically significant relationship between stock market investment frequency and the change in financial outlook.
Step 3: Run a Test of Significance on the Variables
In the GSS 2018 dataset, we would need to identify the relevant variables. For the sake of this example, we assume that:
· Independent Variable (IV): Frequency of stock market investment (which could be captured by a variable such as INVEST in GSS, representing how often a person invests in the stock market).
· Dependent Variable (DV): Change in financial outlook, which could be measured by a variable like FINOUT (self-reported perception of financial situation).
If both variables are continuous (interval/ratio), we would perform regression analysis to assess the relationship between the IV and DV.
Regression Analysis:
1. Variables:
· IV: Frequency of stock market investment
· DV: Change in financial outlook
2. Assumptions:
· Linearity: The relationship between the IV and DV is linear.
· Normality: Both variables follow a normal distribution.
· Homoscedasticity: The variance of the residuals is constant across all levels of the IV.
3. Test Output: The regression output would provide coefficients, t-statistics, and the p-value for the relationship between the IV and DV.
Step 4: Identify the Two-Tailed P Value and Interpret Findings
After running the regression analysis, we would obtain the p-value to assess statistical significance:
· If p < alpha (0.05): We reject the null hypothesis and conclude that there is a statistically significant positive correlation between stock market investment frequency and the change in financial outlook. This suggests that people who invest more frequently in the stock market tend to report a better financial outlook.
· If p ≥ alpha (0.05): We fail to reject the null hypothesis, meaning there is no statistically significant relationship between stock market investment frequency and financial outlook changes.
Conclusion:
· Reject the null hypothesis if p < 0.05, indicating a significant positive relationship between stock market investment and perceived financial outlook.
· Fail to reject the null hypothesis if p ≥ 0.05, indicating no significant relationship.
Step 5: Does the IV Affect the DV?
The regression results will tell us whether the independent variable (frequency of stock market investment) significantly influences the dependent variable (change in financial outlook). If the p-value is below 0.05 and the regression coefficient for the IV is significant, we can conclude that stock market investment frequency does have an effect on individuals' perceived financial outlook.
Why Do We Need to Run Tests of Significance?
Tests of significance, like the regression analysis conducted in this case, help us understand whether the relationship we observe in the sample (the GSS 2018 data) is likely to hold in the larger population. In other words, they allow us to determine if the correlation between stock market investment and financial outlook is a real, meaningful relationship or if it is due to random chance. By comparing the p-value to the alpha level (0.05), we assess whether the observed relationship is statistically significant and generalizable.
If the p-value is less than 0.05, we reject the null hypothesis and conclude that the relationship between investment behavior and financial outlook change is not likely due to chance, thus suggesting a meaningful association
