In-Class Activity 11: Power and Effect Size
As you fill in the information below, please type in RED or BOLD font so it is easy to see.
A. Open SPSS Files
Warning: We are using multiple SPSS datasets for tonight’s activity. Make sure you are using the
correct dataset when answering questions!
We will be using 2 different versions of a prior dataset, NCHS. Both datasets contain similar data
on anthropometry and metabolic variables in a diverse group of subjects. The quantity of subjects and
the variables are different between the two sets. The variable names, labels, and label values have been
meticulously named – please investigate the variables as you use them so you know what you are doing.
B. Body Mass Index and Blood Pressure: Association
Open the data file called NCHS_Reduced.
This data file only contains three variables: ID number (‘SEQN’), Body Mass Index (a measure of obesity,
‘BMI’), and systolic blood pressure (‘SBP’). There are 33 subjects in the file. The goal of this analysis is to
determine if systolic blood pressure is related to body mass index. Here are our hypotheses:
HO: There is no association between Blood Pressure and Body Mass Index
HA: There is an association between Blood Pressure and Body Mass Index
Run a Pearson correlation between Blood Pressure and BMI to investigate our hypothesis.
1) What is the n? What is r? What is the p-value? Which hypothesis is correct (HO or HA)?
2) What is the effect size? What is your interpretation of this effect size?
Now, highlight and copy all three variables for all 33 subjects. Paste the values below your current
subjects to increase your sample size to 66 subjects (this copy/paste procedure works in SPSS just like it
would in Excel). WARNING: SPSS can get unstable when copy/pasting subjects and variables. Make sure
it’s finished processing before you move forward. Repeat the copy/paste of the first 33 subjects to
again increase your sample size. Once your sample size is 99 re-run your Pearson correlation.
3) What is r? What is the new p-value?
4) Did your effect size or interpretation of this effect size change?
Now, highlight and copy all 3 variables for all 99 subjects. Paste the values below your current subjects
to increase your sample size to 198 subjects. Once your sample size is 198 re-run the correlation.
5) What is r? What is the new p-value? Which hypothesis is correct (HO or HA)?
6) Did your effect size or interpretation of this effect size change?
7) If you did this correctly, each time you re-ran the correlation the p-value should have changed but the
correlation and effect size should have remained nearly constant. Why? What does this demonstrate to
you about statistical power? About effect size?
C. Weight Status and Total Cholesterol: Group Differences
Change datasets to NCHS_Full_Power
Our new analysis will deal with total cholesterol and weight status. In this dataset there are three
groups of weight status, Normal Weight, Overweight, and Obese. Total cholesterol is one of the
strongest risk factors for cardiovascular disease and should be related to weight status. Our hypotheses:
HO: Total cholesterol will not be different between the three groups of weight status
HA: Total cholesterol will be different between the three groups of weight status
Run a One-Way ANOVA comparing total cholesterol (variable name ‘TChol’) between these three groups
of weight status. Total Cholesterol is the dependent variable, and Weight Status is the independent
variable (I recommend requesting descriptive statistics too, so you can ‘see’ the data).
8) Was the ANOVA statistically significant? What was the p-value? Should we accept the null or the
alternative hypothesis?
Split the file by race/ethnicity. Go to ‘Data’ > ‘Split File’ > ‘Compare Groups’ and move ‘race/ethnicity’
into the box. Now, re-run the same ANOVA. This will provide you with three ANOVA’s comparing
weight status groups for total cholesterol within all three groups of race/ethnicity.
9) Re-assess the situation. What were the p-values? Should we accept the null or the alternative
hypothesis?
10) Why has your decision changed between #8 and #9? What did we accomplish by separating the
sample by race/ethnicity?
E. Men vs. Women: HDL Cholesterol
Un-do the file split by race/ethnicity. Continue to use the same dataset – NCHS_Full_Power.
It’s well known that women tend to have higher HDL-cholesterol values than men (HDL is the ‘good’
cholesterol). Use the most appropriate statistical test to compare these two groups.
11) Is it true? Do women have higher HDL cholesterol values than men?
12) What is the mean difference?
13) What is the p-value for this comparison?
Notice that we have about 1000 subjects in this analysis – such a comparison will almost always be
statistically significant because we have such a high statistical power. Because of this it would be a good
idea to report effect size along with the p-value. Calculate the appropriate effect size (hint: you should
use a standard deviation of 15 for your calculation – this is the standard deviation of HDL in the total
sample).
14) What is the effect size?
15) Interpret the effect size – what does this mean? I know these groups are ‘significantly’ different, but
is this difference ‘meaningful’?
16) Save this word file, do your homework, and let me know it’s completed before next class.