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Posted 09-10-2020 11:42 PM
(774 views)

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 Effect H = Type III SSCP Matrix for testscore E = Error SSCP Matrix S=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 Effect H = Type III SSCP Matrix for testscore*Condition E = Error SSCP Matrix S=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, Repeated The 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 of Condition | 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

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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?

--

Paige Miller

Paige Miller

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@zahidhasandipu wrote:

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

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Paige Miller

Paige Miller

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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.

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