4.5: Descriptive Statistics Separately for Each Group
- Page ID
- 29453
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)It is very commonly the case that you find yourself needing to look at descriptive statistics broken down by some grouping variable. This is pretty easy to do in SPSS.
To illustrate, open the ClinicalTrial.sav
data file in SPSS. In this data file we have the results of a fake study with fake data examining the effects of different antidepressant drugs (placebo, anxifree, and joyzepam) and type of psychotherapy (none or CBT). The outcome variable is gain in mood following treatment (mood.gain). The design for the study looks something like this:
Therapy Used | |||
---|---|---|---|
No Therapy | CBT | ||
Medication Used | Placebo | ||
Anxifree | |||
Joyzepam |
The cells in the body of the table, then, would contain the mean gain in mood for each group. So, let's see how SPSS can give us those answers.
Click on Analyze, then Descriptive Statistics, then Explore.
Figure \(\PageIndex{i}\): Opening the Explore dialog in SPSS. (Copyright; author via source)
You will then see something like this:
Figure \(\PageIndex{i}\): The Explore dialog in SPSS. (Copyright; author via source)
Remember the discussion of predictor and outcome variables in chapter ***? OK, so here, SPSS refers to the outcome variables as Dependent variables and the predictor variables as Factors. Why? Well, probably just to confuse us all. Given that, the drug used and the therapy used are the predictor variables and the gain in mood is the outcome variable. Let's say you are interested in finding out the mean Gain in Mood depending on the type of therapy used. To do that, select the Gain in Mood variable and move it to the Dependent List and select Therapy Used and move it to the Factor List. Once that is complete, you should see this:
Since we are lazy and don't feel like changing any of the default settings, which are actually pretty good, just click OK. In the output window you'll now see a whole lot of things, but among them is the descriptive statistics table here:
I have highlighted the means for Gain in Mood for each of the two levels of therapy. The mean Gain in Mood for No Therapy is .7222, while the mean Gain in Mood for the group that received CBT therapy is 1.044. We don't know yet if that is a meaningful difference (more analyses later), but now we know the means and well as pretty much every other descriptive statistic of interest for Gain in Mood broken down by therapy group.
What if you have multiple grouping variables? Suppose, for example, you would like to look at the average mood gain separately for all possible combinations of drug and therapy. We can glean this from SPSS using the General Linear Model option and performing a Univariate Analysis of Variance. But that's not important right now. For now, just look at the descriptive stats for all the possible combinations:
In a later chapter we will examine ANOVA in depth. Just not yet.