Multiple Regression (SPSS): 1 Paragraph Response to Classmate’s Discussion Post

By Day 5

Respond to at least one of your colleagues’ posts and comment on the following:

1. Do you think the variables are appropriately used? Why or why not?
2. Does the addition of the control variables make sense to you? Why or why not?
3. Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.
4. If there was a significant effect, comments on the strength and its meaningfulness.
5. As a lay reader, were you able to understand the results and their implications? Why or why not?

Classmate’s Post (Melvin):

“Independent variables (IV1 and IV2) and their Level of Measurement.

The 2 independent variables and their level of measurement are the “WWW HOURS PER WEEK” and “Rs occupational prestige score (2010)”.

Dependent variable (DV) and its Level of Measurement.

The dependent variable and its level of measurement is “R’s socioeconomic index (2010)”

Research Question

What is the relationship between www hours per week and respondent’s occupational prestige score (2010) and the respondent’s socioeconomic index (2010)?

Null hypothesis

There is no relationship between www hours per week and respondent’s occupational prestige score (2010) and the respondent’s socioeconomic index (2010).

Research design

The research design is a multiple regression model to estimate how several independent variables affect one dependent variable (Frankfort-Nachmias & Leon-Guerrero, 2018). Multiple regression models build on bivariate regression by adding more predictor variables to the equation (Laureate Education, Inc., 2016). Following Laureates information on R and R² it is safer to use the adjusted R² when using multiple predictors. We can interpret the model as followed: 70% “of the variability in a respondent’s socioeconomic status index is explained by the combination of www hours per week and occupational prestige score. If we follow Evans (1996), guide that r: • .00-.19 “very weak” • .20-.39 “weak” • .40-.59 “moderate” • .60-.79 “strong” • .80-1.0 “very strong” (Beldjazia, & Alatou, 2016). We can conclude since R = .834 (80%) this makes it a very strong linear relationship and the R² = .696 (70%), would make it a strong linear relationship between www hours per week and respondent occupational prestige score (2010) and the respondent’s socioeconomic index (2010)? By examining the Anova table we can test the significance of the regression model. Since the SIG. = .000, the null hypothesis is rejected, which confirms there is statistical significance. Thus, confirming there is no relationship between www hours per week and respondent’s occupational prestige score (2010) and the respondent’s socioeconomic index (2010). Under the coefficients we can see how the constant is serving as a mathematical anchor where a regression line crosses the y-axis (Laureate Education Inc., 2016).

Coefficients Output Levels of examination

Standardized coefficients tell us that when the one standard deviation unit increases in the beta, the dependent variable will change by the beta’s value in standard deviations (Laureate Education Inc., 2016). For every one unit of increase in respondent’s occupation prestige score the respondent’s socioeconomic index will change by 1.369 units controlling for their www hours per week, and for every additional www hours per week increase the respondent’s socioeconomic index will change by .103 controlling the occupational prestige score. The standard deviation increases in respondent’s occupational prestige score, their socioeconomic index, the dependent variable, will change by 0.830 standard deviations, controlling for the www hours per week. Also, every time additional hours per week are added, respondents’ socioeconomic index will change by .069 units, controlling for their occupational prestige score. Since the SIG. =.000 in the coefficients table, we can also reject the null hypothesis and conclude that there is no relationship www hours per week and respondent’s occupational prestige score (2010) and the respondents socioeconomic index (2010), and both therespondents’ occupational prestige score and www hours per week are statistically significant predictors of the respondents’ socioeconomic index.

Regression Equation

R’s socioeconomic index (2010) = -14.232 + (1.369) Rs occupational prestige score (2010) + (.103) WWW HOURS PER WEEK

Respondent’s socioeconomic index (2010) = -14.232 + 1.369 + .103.