# Response to Classmate s Post SPSS

#### By Day 5

Respond to at least one of your colleaguesâ€™ posts and comment on the following:

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

Classmate’s Post (Natalie):

Variables

The independent variable for the Pearson Correlation test using the General Social Survey dataset is â€œhighest year of school completedâ€ which is measured on an interval/ratio scale. The dependent variable is â€œrespondentâ€™s socioeconomic indexâ€ which is also measured on an interval scale. The Pearson correlation test is easier to understand when using two metric level variables (Laureate Education (Producer) (2016b).

Research Question

What is the relationship between the respondentâ€™s highest year of school completed and the respondentâ€™s socioeconomic index?

Null Hypothesis

There is no relationship between the respondentâ€™s highest year of school completed and the respondentâ€™s socioeconomic index.

Research design

This correlational research design seeks to statistically measure the strength of linear relationship among the respondentâ€™s highest year of school completed and the respondentâ€™s socioeconomic index. A Pearson Correlation was conducted to compare the highest year of school completed and the respondentâ€™s socioeconomic index. Based on the Pearson Correlation test (Table 1), the correlation coefficient is 0.581 between the highest year of school completed and the respondentâ€™s socioeconomic index. The Pearson correlation coefficient of .581 has a positive linear relationship and the relationship is somewhat moderate. The Pearson Correlation Coefficient ranges from -1.0 to 1.0 with zero indicating â€œno relationshipâ€. The closer the Coefficient moves to -1.0 or 1.0, the stronger the relationship (discussed below under Effect Size). The p-value is .000 which is below the alpha level therefore we can reject the null hypothesis and conclude that there is no relationship between the highest year of school completed and the respondentâ€™s socioeconomic index. This correlation is significant at the .01 level.

The Model Summary table (Table 2) shows the Pearson Correlation Coefficient of 0.581. From the R square figure of .337 the researcher can state that 33% of the respondentâ€™s socioeconomic status is accounted for by their highest year of school completed. The ANOVA table (Table 3) test the overall significance of the regression model. The p-value is 0.000 which is below the alpha level, therefore the model has statistical significance and the R square can be interpreted. Taking a look at the Coefficients output (Table 4), the first set of statistics under the constant model shows where the slope of our regression line intercepts with the Y-axis. The second set of statistics under the independent variable â€œhighest year of school completedâ€ shows that for every additional year of school completed, socioeconomic status will change by 4.260 units on average. The Standardized Coefficients Beta of 0.581 is the same figure as the Pearson Correlation Coefficient as it standardizes the units of measure. The significance level of 0.000 is below the alpha level, therefore reject the null hypothesis and conclude that there is no relationship between the two variables. The more years of school completed on average, the higher their socioeconomic index will be.

Effect Size

The coefficient of determination also denoted by the R2 value is also used as the effect size (Beldjazia & Alatou, 2016). With the R2, of .337 the researcher can state that the highest year of school explains 34% of the variation in the respondentâ€™s socioeconomic index. The researcher can also state that by using the highest year of school and the linear production rule to predict the respondentâ€™s socioeconomic index, we have reduced the error of prediction by 34% (Frankfort-Nachmias & Leon-Guerrero, 2018, p. 341).

It was also noted by Frankfort-Nachmias & Leon-Guererro (2018) that an R2 near zero indicates a poor fit whereas an R2 closer to 1.0 provides a good fit (p. 341). Also by using the guide that Evans (1996) suggested for the absolute value of r:

1. – 0.19 is very weak, 0.20 – 0.39 is weak, 0.40 – 0.59 is moderate, 0.60 – 0.79 is strong, and 0.80 â€“ 1.0 is very strong,

The researcher can say that the R2 value of .337 would be a poor fit or have a â€œweak linear correlationâ€. There is a weak linear relationship between the highest year of school completed and the respondentâ€™s socioeconomic index.

 Correlations HIGHEST YEAR OF SCHOOL COMPLETED R’s socioeconomic index (2010) HIGHEST YEAR OF SCHOOL COMPLETED Pearson Correlation 1 .581** Sig. (2-tailed) .000 N 2537 2426 R’s socioeconomic index (2010) Pearson Correlation .581** 1 Sig. (2-tailed) .000 N 2426 2427 **. Correlation is significant at the 0.01 level (2-tailed).

Table 1

 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .581a .337 .337 18.2436 a. Predictors: (Constant), HIGHEST YEAR OF SCHOOL COMPLETED

Table 2

 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 410356.111 1 410356.111 1232.935 .000b Residual 806776.546 2424 332.829 Total 1217132.657 2425 a. Dependent Variable: R’s socioeconomic index (2010) b. Predictors: (Constant), HIGHEST YEAR OF SCHOOL COMPLETED

Table 3

 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -12.603 1.710 -7.368 .000 HIGHEST YEAR OF SCHOOL COMPLETED 4.260 .121 .581 35.113 .000 a. Dependent Variable: R’s socioeconomic index (2010)