SPSS Boxplots for More Than One Variable

SPSS Boxplots for More Than One Variable

An ANOVA with repeated measures is for comparing three or more group means where the participants are the same in each group. This usually occurs in two situations - when participants are measured multiple times to see changes to an intervention or when participants are subjected to more than one condition/trial and the response to each of these conditions wants to be compared. For a complete guide on ANOVA with Repeated Measures, please go to our guide here.
Heart disease is one of the largest causes of premature death and it is now known that chronic, low-level inflammation is a cause of heart disease. Exercise is known to have many benefits including protection against heart disease. A researcher wished to know whether this protection against heart disease might be afforded by exercise reducing inflammation. The researcher was also curious as to whether this protection might be gained over a short period of time or whether it took longer. In order to investigate this idea the researcher recruited 20 participants who underwent a 6-month exercise training program. In order to determine whether inflammation had been reduced, he measured the inflammatory marker called CRP pre-training, 2 weeks into training and post-6-months-training.
Test Procedure in SPSS
[To know how to correctly enter your data into SPSS in order to run a repeated measures ANOVA please read our Entering Data in SPSS tutorial.]
Click Analyze > General Linear Model > Repeated Measures... on the top menu as shown below:

Published with written permission from SPSS Inc, an IBM Company.
You will be presented with the following screen:

Published with written permission from SPSS Inc, an IBM Company.
In the "Within-Subject Factor Name:" replace "factor1" with a name that is more meaningful name for your independent variable. In our example, we will call our within-subject factor name "time" as it represents the different times that we took CRP measurements from our participants (pre-training, pre + 2 weeks and post-training).
Enter into the "Number of Levels:" box the number of times the dependent variable has been measured. In this case, enter "3", representing pre-training, pre + 2 weeks and post-training.
Click the button.
Put an appropriate name for your dependent variable in the "Measure Name:" box. In this case we have labelled our dependent variable CRP.
Click the button.
You will be presented with the diagram screen below:

Published with written permission from SPSS Inc, an IBM Company.
Click the button and you will be presented with the following screen:

Published with written permission from SPSS Inc, an IBM Company.
Transfer "Pre_Training", "Week2" and "Post_Training" into the "Within-Subjects Variables (time):" box by either drag-and-dropping or using the button. If you make a mistake you can use the and buttons to reorder your variables.

Published with written permission from SPSS Inc, an IBM Company.
Click the button. You will be presented with the following screen:

Published with written permission from SPSS Inc, an IBM Company.
Transfer the "time" factor from the "Factors:" box into the "Horizontal Axis:" box by either drag-and-drop or the button.
Click the button. You will be presented with the following screen:

Published with written permission from SPSS Inc, an IBM Company.
Click the button.

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Correct data formatting for a repeated-measures ANOVA in SPSS

Correct data formatting for a repeated-measures ANOVA in SPSS

Overview: The instructions on this sheet cover two procedures: 
1. A one-way within-subjects ANOVA, used when you have one independent variable and one group of subjects measured repeatedly under 3 or more conditions. For example, subjects are measured in a baseline condition, are given a treatment, and are followed up at 3 later points in time. 
2. A 2-way mixed ANOVA, used when you have two independent variables with one within-subjects factor, and one between-subjects factor. The within-subjects factor is the repeated measures factor. On the between-subjects factor, subjects are divided into discrete subgroups, and each subject falls into only one of those subgroups.
To run: From the Data Editor Window 
Click on "Analyze" 
Click on "General Linear Model" 
Click on "Repeated Measures"

The following dialog box will appear:

Begin with the repeated measures factor. The first thing you need to do is to instruct SPSS to treat your repeated measurements not as different variables, but as different levels of the same variable. To do this click the box "Within-Subject Factor Name" and type in a name for your repeated measures variable.

Type in the number of observations you have on each subject in the box labeled, "Number of Levels" 
Click on "Add". The name of your new variable will appear in the box with the number of levels of that variable in parentheses.

Click on "Define" 
The following GLM - Repeated Measures dialog box will appear:

You must now tell SPSS what the different levels of your repeated measures factor are, that is, which variables from the column on the left represent your different levels. Click on each of the variables, i.e. baseline, time2, time3, time4 and click the right arrow key to move those variables to the box labeled, "Within-subjects Variables"

If you are running a one-way repeated measures ANOVA, you are done. 
Click "OK"

If you are running a two-way mixed ANOVA you need to indicate which variable is your between subjects variable. Click on that variable from the column on the left and click the right arrow to move it to the box on the right labeled, "Between-Subjects Factor(s):"

Click on "OK"

Output:

One-way repeated measures ANOVA 
The first table you see lists "Descriptive Statistics" for each of your groups, i.e., mean, standard deviation, and sample size. Note the sample size will be identical for all groups because the same subjects appear in each group.

Look at the table, "Tests of within-subjects effects." 
The first line of this table gives you your F, its degrees of freedom, and the probability of your F.

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SELECTED SPSS OUTPUT FOR ONEWAY ANCOVA

SELECTED SPSS OUTPUT FOR ONEWAY ANCOVA

Descriptive statistics for each category of the independent variable appear in Table 9.15, labeled "Descriptive 
Statistics." The "Tests of Between-Subjects Effects" table (Table 9.16) lists both the independent variable, in 
this case, technique, and the covariate, in this case health, as predictors of the dependent variable. The values 
used to determine whether changes in heart rate differ significantly with respect to the independent variable 
and considering the possible effects of the covariate appear in the top row of this table.
According to results of this analysis, those exposed each of the three relaxation techniques 
did not experience significantly different changes in heart rate. The p value of .183 lies 
above the standard α of .05 as well as above an elevated α of .10, indicating that one would 
accept the null hypothesis of equality at these levels of significance. The analysis 
considered differences in the overall health of patients in the three independent-variable 
conditions when calculating these results, hence the designation of a Type III Sum of 
Squares value in the "Tests of Between-Subjects Effects" table. ▄ 
The process used to request and analyze SPSS results of an ANCOVA translate easily into a 
MANCOVA. Performing a MANCOVA in SPSS requires the same steps, only you would need 
to use SPSS's Multivariate, rather than Univariate window. In the Multivariate window, you 
can identify as many dependent variables as needed for the analysis. SPSS assembles the 
values for the dependent variables into canonical variate scores. By inputting names of 
covariates into the "Covariate(s)" box, you tell SPSS to consider the roles of these covariates 
upon the relationship between the independent variables and the canonical variate.
The MANCOVA output that results contains a "Multivariate Tests" table. This table 
resembles the "Multivariate Tests" table produced for a MANCOVA, however, it also 
includes the names of covariates. Assuming you wish to consider results based upon the 
Wilks' Lambda procedure for obtaining F, you should focus upon values in this row of the 
table. A p-value that exceeds α indicates significant differences between mean canonical 
variate values for the covariate-biased independent-variable categories.

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how carry out single sample t test SPSS , R commander & R

how carry out single sample t test SPSS , R commander & R

how carry out single sample t test SPSS , R commander & R
Paired-Sample T-Test is also known as dependent T-Test, repeated-measures T-test or within-subjects T-test. A Paired-sample t-test is used to analyse paired scores, specifically, we want to see if there is difference between paired scores.

Example Scenario
A new fitness program is devised for obese people. Each participant's weight was measured before and after the program to see if the fitness program is effective in reducing their weights.

In this example, our null hypothesis is that the program is not effective, i.e., there is no difference between the weight measured before and after the program. The alternative hypothesis is that the program is effective and the weight measured after is less than the weight measured before the program. The dataset can be obtained here.

In the data, the first column is the weight measured before the program and the second column is the weight after.

Step 1
Select "Analyze -> Compare Means -> Paired-Samples T Test".

A new window pops out. Drag the variable "Before" and "After" from the list on the left to the pair 1 variable 1 and variable 2 respectively, as shown below. Then click "OK".

Step 2
The results now pop out in the "Output" window.

Step 4
We can now interpret the result.

From A, since the p-value is 0.472, we reject the alternative hypothesis and conclude that the fitness program is not effective at 5% significant level.

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Testing of Mediation Models in SPSS and SAS

Testing of Mediation Models in SPSS and SAS

Most research focuses on relations between two variables, X and Y, and much has been written about two-variable relations, including conditions under which X can be considered a possible cause of Y. These conditions include randomization of units to values of X and independence of units across and within values of X. Mediation in its simplest form represents the addition of a third variable to this X → Y relation, whereby X causes the mediator, M, and M causes Y, so X → M → Y. Mediation is only one of several relations that may be present when a third variable, Z (using Z to represent the third variable), is included in the analysis of a two-variable system. One possibility is that Z causes both X and Y, so that ignoring Z leads to incorrect inference about the relation of X and Y; this would be an example of a confounding variable. In another situation, Z may be related to X and/or Y, so that information about Z improves prediction of Y by X, but does not substantially alter the relation of X to Y when Z is included in the analysis; this is an example of a covariate. Z may also modify the relation of X to Y such that the relation of X to Y differs at different values of Z; this is an example of a moderator or interaction effect. The distinction between a moderator and mediator has been an ongoing topic of research (Baron & Kenny 1986, Holmbeck 1997, Kraemer et al. 2001). A mediator is a variable that is in a causal sequence between two variables, whereas a moderator is not part of a causal sequence between the two variables. More detailed definitions of these variables in a three-variable system may be found in Robins & Greenland (1992).

The single-mediator model is shown in Figure 1, where the variables X, M, and Y are in rectangles and the arrows represent relations among variables. Figure 1 uses the notation most widely applied in psychology, with a representing the relation of X to M, b representing the relation of M to Y adjusted for X, and c′ the relation of X to Y adjusted for M. The symbols e2 and e3 represent residuals in the M and Y variables, respectively. The equations and coefficients corresponding to Figure 1 are discussed below. For now, note that there is a direct effect relating X to Y and a mediated effect by which X indirectly affects Y through M. Given that most prior mediation research has applied this single-mediator model, this review starts with this model. Limitations and extensions of the model are described in subsequent sections.

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