Respond to at least one of your colleagues’ posts and respond based 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 to 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 (Natalie) Post:

“Variables

The dependent variable identified from the General Social Survey dataset is “RS OCCUPATIONAL PRESTIGE SCORE 2010” which is measured on the interval/ratio scale. The independent variable identified from the same dataset is “RS HIGHEST DEGREE” which has more than 3 levels and is measured on the nominal scale.

Research Question

Is there a difference in the respondent’s occupational prestige score based on the respondent’s highest degree?

Null Hypothesis

There is no difference in the respondent’s occupational prestige score based on the respondent’s highest degree.

Research design

This research can either be experimental or non-experimental but the primary data analysis is of group comparison. A non-experimental design can be descriptive, predictive, or explanatory. It however remains quantitative in nature as it seeks to examine the equality of population means for a quantitative outcome and a single categorical explanatory variable with any number of levels. The one-way ANOVA was therefore conducted to compare the respondent’s occupational prestige score based on the respondent’s highest degree. SPSS specifies the value as Sig. Therefore, the value is 0.000 (Table 1), which is less than . Based on this value, the researcher has strong evidence to reject the null hypothesis and determine that the ANOVA is statistically significant. The multiple comparisons table (Table 2) also shows that the comparisons between each level of the independent variable, “RS HIGHEST DEGREE”, is also statistically significant as the Sig. values are all less than .

Effect Size

The correlation ratio or eta square (η² ) allows the researcher to make a statement about the strength of the relationship or the effect size (Frankfort-Nachmias & Leon-Guerrero, 2018, p. 312). We calculate the effect size as follows:

η² = SSB / SST

Where SSB is the between-group sum of squares and SST is the total sum of squares

η² = 134134.992 / 443145.274

η²= 0.3

An eta square of 0 would mean no differences (and no influence), while 1 would indicate a full dependency. In the example the eta-squared is .3. We can therefore say that 30% of the variation in the dependent variable can be explained by the independent variable. The sample mean is 0.3 standard deviations lower than the population mean. Suggested norms for partial eta-squared: small = 0.01; medium = 0.06; large = 0.14. The researcher therefore compares the absolute value to the key for effect sizes, it can be concluded that the effect size is large.

Table 1

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