Response to Classmate’s Post (SPSS)
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By Day 5
Respond to at least one of your colleagues’ posts and comment on the following:
 Do you think the variables are appropriately used? Why or why not?
 Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.
 If there was a significant effect, comment on the strength and its meaningfulness.
 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 “highest year of school completed” which is measured on an interval/ratio scale. The dependent variable is “respondent’s socioeconomic index” 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 respondent’s highest year of school completed and the respondent’s socioeconomic index?
Null Hypothesis
There is no relationship between the respondent’s highest year of school completed and the respondent’s socioeconomic index.
Research design
This correlational research design seeks to statistically measure the strength of linear relationship among the respondent’s highest year of school completed and the respondent’s socioeconomic index. A Pearson Correlation was conducted to compare the highest year of school completed and the respondent’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 respondent’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 “no relationship”. The closer the Coefficient moves to 1.0 or 1.0, the stronger the relationship (discussed below under Effect Size). The pvalue 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 respondent’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 respondent’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 pvalue 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 Yaxis. The second set of statistics under the independent variable “highest year of school completed” 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 R^{2} value is also used as the effect size (Beldjazia & Alatou, 2016). With the R^{2, }of .337 the researcher can state that the highest year of school explains 34% of the variation in the respondent’s socioeconomic index. The researcher can also state that by using the highest year of school and the linear production rule to predict the respondent’s socioeconomic index, we have reduced the error of prediction by 34% (FrankfortNachmias & LeonGuerrero, 2018, p. 341).
It was also noted by FrankfortNachmias & LeonGuererro (2018) that an R^{2 }near zero indicates a poor fit whereas an R^{2 }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:
 – 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 – 1.0 is very strong,
The researcher can say that the R^{2 }value of .337 would be a poor fit or have a “weak linear correlation”. There is a weak linear relationship between the highest year of school completed and the respondent’s socioeconomic index.
Correlations 

HIGHEST YEAR OF SCHOOL COMPLETED 
R’s socioeconomic index (2010) 

HIGHEST YEAR OF SCHOOL COMPLETED 
Pearson Correlation 
1 
.581^{**} 
Sig. (2tailed) 
.000 

N 
2537 
2426 

R’s socioeconomic index (2010) 
Pearson Correlation 
.581^{**} 
1 
Sig. (2tailed) 
.000 

N 
2426 
2427 

**. Correlation is significant at the 0.01 level (2tailed). 
Table 1
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.581^{a} 
.337 
.337 
18.2436 
a. Predictors: (Constant), HIGHEST YEAR OF SCHOOL COMPLETED 
Table 2
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
410356.111 
1 
410356.111 
1232.935 
.000^{b} 
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
Coefficients^{a} 

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) 
“
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