Part 1:
Interpret an ANOVA in SPSS:
Introduction:
You are interested in the importance of email in modern American society.You wonder why written correspondence (even if it’s electronically transferred) is still so central to the lives of Americans given the widespread use of cell phones and the ease of calling, texting, and video chats.You believe that the use of email as a means of formal “work correspondence” might explain its continued prominence.Therefore, you hypothesize that email is likely used more for business than for personal use.To test this hypothesis, you examine two variables from a sample of Americans in the 2016 General Social Survey.
Your dependent variable, number of hours spent using email per week (emailhrin GSS), is a continuous variable.Your independent variable, work status (wrkstsin GSS), measured as “full-time,” “part-time,” or “not employed” is discrete.Based on the level of measurement of your variables, you decide to perform an ANOVA to test whether work status is significantly related to hours spent emailing per week in the population.You want to be 95% confident that any relationship you might find in the GSS sample is also true for the US population in 2016.Your null hypothesis is that there is no relationship in the population between work status and number of hours spent emailing (i.e. the mean hours of emailing in the population is the same for all three categories of work status).Your alternative hypothesis is that full-time workers use email more often than part-time workers who use email more often than those that are not working.
1.First you create a bar chart in SPSS to jointly describe your variables to see if it looks like there is a relationship in your sample.
Based on the bar chart that you’ve produced, does it look like there’s a relationship between work status and the number of hours spent emailing per week in the sample? Explain. How does average number of hours spent emailing each week change by work status?
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2.Next you perform an ANOVA in SPSS and determine if there are differences in email use by job status in the population.
ANOVA
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EMAIL HOURS PER WEEK
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Sum of Squares
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df
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Mean Square
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F
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Sig.
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Between Groups
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12351.413
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2
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6175.706
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51.297
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.000
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Within Groups
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176492.124
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1466
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120.390
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Total
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188843.537
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1468
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Based on the ANOVA output that you’ve produced above…
What is the value of the F-ratio for this model? ___________
Is there a significant relationship between work status and email usage in the 2016 US population (to 95% confidence)? Explain your answer._______________________________
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3.Calculate the strength of the relationship called eta-squared using the chart above.
a.Look in your output table for two numbers: “Between Groups” and “Total” in the “Sum of Squares” column.
b.To find eta-squared, divide the value for between groups by the value for total and write your answer below (rounding to two decimal places is fine).
What is the value for eta-squared? ___________
What is the strength of the relationship (weak? moderate? strong?) and why?
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