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 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 “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 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 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 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 “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 R² 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% (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 – 1.0 is very strong,

The researcher can say that the R² 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.

Table 1

Table 2

Table 3

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