Calcite | Level 5

## GLM

I know this is really a long and complicated result, still, I would really appreciate your help.

The hypothesis is students who use software after college known as condition 1 would perform better than the in-class group known as condition 0.  To test this, students were given a math test during the first week of a month, and the same test again during the first week of another month.
How should I know whether to accept/reject the hypothesis and is there a change in the student's scores?

Result:

GLM, Repeated Sunday, August 23, 2020 05:52:05 PM 1
The GLM Procedure

 Class Level Information Class Levels Values Condition 2 0 1

 Number of Observations Read 353 Number of Observations Used 337

GLM, Repeated Sunday, August 23, 2020 05:52:05 PM 2
The GLM Procedure
Repeated Measures Analysis of Variance

 Repeated Measures Level Information Dependent Variable pretest posttest Level of testscore 1 2

 MANOVA Test Criteria and Exact F Statistics for the Hypothesis of no testscore EffectH = Type III SSCP Matrix for testscoreE = Error SSCP MatrixS=1 M=-0.5 N=166.5 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.96161567 13.37 1 335 0.0003 Pillai's Trace 0.03838433 13.37 1 335 0.0003 Hotelling-Lawley Trace 0.03991649 13.37 1 335 0.0003 Roy's Greatest Root 0.03991649 13.37 1 335 0.0003

 MANOVA Test Criteria and Exact F Statistics for the Hypothesis of no testscore*Condition EffectH = Type III SSCP Matrix for testscore*ConditionE = Error SSCP MatrixS=1 M=-0.5 N=166.5 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.99435945 1.90 1 335 0.1690 Pillai's Trace 0.00564055 1.90 1 335 0.1690 Hotelling-Lawley Trace 0.00567255 1.90 1 335 0.1690 Roy's Greatest Root 0.00567255 1.90 1 335 0.1690

GLM, Repeated Sunday, August 23, 2020 05:52:05 PM 3
The GLM Procedure
Repeated Measures Analysis of Variance
Tests of Hypotheses for Between Subjects Effects

 Source DF Type III SS Mean Square F Value Pr > F Condition 1 418.010493 418.010493 17.48 <.0001 Error 335 8009.641852 23.909379

 GLM, RepeatedThe GLM Procedure Sunday, August 23, 2020 05:52:05 PM 4

Repeated Measures Analysis of Variance
Univariate Tests of Hypotheses for Within Subject Effects

 Source DF Type III SS Mean Square F Value Pr > F testscore 1 92.151681 92.151681 13.37 0.0003 testscore*Condition 1 13.095717 13.095717 1.90 0.1690 Error(testscore) 335 2308.611583 6.891378

GLM, Repeated Sunday, August 23, 2020 05:52:05 PM 5

GLM, Repeated Sunday, August 23, 2020 05:52:05 PM 6

 pretest posttest Level ofCondition N Mean Std Dev Mean Std Dev 0 148 68.3993243 4.73396321 68.8635135 3.92975997 1 189 69.7052910 3.44273418 70.7312169 3.66520864

5 REPLIES 5
Diamond | Level 26

## Re: GLM

It always helps to show your code. Obviously, you have used MANOVA here, which does not seem to fit the problem description.

Then you say:

The hypothesis is students who use software after college known as condition 1 would perform better than the in-class group known as condition 0.  To test this, students were given a math test during the first week of a month, and the same test again during the first week of another month.

but I'm not sure what you mean. Do you mean that students in condition 1 performed higher on the tests ON AVERAGE than condition 0, or do you mean that condition 1 gained more across the two time periods than condition 0, or do you mean something else?

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Paige Miller
Calcite | Level 5

## Re: GLM

I am trying to interpret the results that are given. What could be the code to get a similar kind of results from any data set?

Diamond | Level 26

## Re: GLM

@zahidhasandipu wrote:

I am trying to interpret the results that are given. What could be the code to get a similar kind of results from any data set?

This far too brief and far too vague a question for us to give an answer. Please elaborate.

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Paige Miller
Super User

## Re: GLM

If you have identification information to compare individual students scores this looks like a Paired T-test of means to determine the mean change in scores might well be appropriate.

Jade | Level 19

## Re: GLM

Between @ballardw and @PaigeMiller , you should get the idea that a repeated measures analysis in GLM using the MANOVA approach is like swatting a fly with an AK-47.  For this design, a paired t-test (using PROC TTEST) will more clearly answer your question.

As an aside, you probably shouldn't be using GLM for analysis of any repeated measures design in any case.  The assumptions that go into the analysis are seldom met, the standard errors of the main effects in the model are miscalculated, and you need to have a pretty strong background in multivariate analysis to interpret the results.  PROC TTEST for simple pre vs post tests in two groups, or PROC MIXED for more complex repeated measures are preferred.

SteveDenham

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