Statistical Analysis of Treatment and Gender Effects on Emotion and Worry: A Two-Way Factorial MANOVA
Statistical evaluation of gender differences in emotional responses to treatment.
Benjamin Fisher
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Statistical Analysis of Treatment and Gender Effects on Emotion and
Worry: A Two-Way Factorial MANOVA
1. What are the independent variables in this study? What are the dependent variables?
Independent variables: TREATMENT, GENDER
Dependent variables: EMOTION, WORRY
2. Why is a factorial MANOVA appropriate to use for this research design?
There are two reasons why MANOVA is better for this research design:
(1) MANOVA is a more powerful statistical technique because it is better able to detect
differences when such difference do exist, when compared to a series of ANOVAS.
(2) MANOVA provides a way to control inflated type I error.
3. Did you find any errors that the researcher made when setting up the SPSS data file
(don't forget to check the variable view)? If so, what did you find? How did you correct it?
HINT:Yes, there are coding errors for Measures.
Treatment and Gender variables should be nominal, not ordinal.
The Emotion and Worry variables are not labeled.
4. Perform Initial Data Screening. What did you find regarding missing values, univariate
outliers, multivariate outliers, normality?
HINT: Revisit instructions from last module's readings on how to compute Mahalanobis
distance and then analyze for multivariate outliers.
MISING VALUES:There are no missing values.
Univariate Statistics
N Mean Std.
Deviation
Missing No. of Extremesa
Count Percent Low High
Treatme
nt
100 2.00 .829 0 .0 0 0
Gender 100 .50 .503 0 .0 0 0
Worry: A Two-Way Factorial MANOVA
1. What are the independent variables in this study? What are the dependent variables?
Independent variables: TREATMENT, GENDER
Dependent variables: EMOTION, WORRY
2. Why is a factorial MANOVA appropriate to use for this research design?
There are two reasons why MANOVA is better for this research design:
(1) MANOVA is a more powerful statistical technique because it is better able to detect
differences when such difference do exist, when compared to a series of ANOVAS.
(2) MANOVA provides a way to control inflated type I error.
3. Did you find any errors that the researcher made when setting up the SPSS data file
(don't forget to check the variable view)? If so, what did you find? How did you correct it?
HINT:Yes, there are coding errors for Measures.
Treatment and Gender variables should be nominal, not ordinal.
The Emotion and Worry variables are not labeled.
4. Perform Initial Data Screening. What did you find regarding missing values, univariate
outliers, multivariate outliers, normality?
HINT: Revisit instructions from last module's readings on how to compute Mahalanobis
distance and then analyze for multivariate outliers.
MISING VALUES:There are no missing values.
Univariate Statistics
N Mean Std.
Deviation
Missing No. of Extremesa
Count Percent Low High
Treatme
nt
100 2.00 .829 0 .0 0 0
Gender 100 .50 .503 0 .0 0 0
Emotion 100 27.64 13.977 0 .0 0 0
Worry 100 34.88 8.985 0 .0 1 0
a. Number of cases outside the range (Q1 - 1.5*IQR, Q3 + 1.5*IQR).
OUTLIERS. From the box plot, there is one outlier for WORRY. Observation No. 96, value
= 8.
Residuals Statisticsa
Minimum Maximum Mean Std.
Deviation
N
Predicted Value -.44 1.19 .50 .432 100
Std. Predicted Value -2.168 1.591 .000 1.000 100
Standard Error of
Predicted Value
.028 .092 .051 .011 100
Worry 100 34.88 8.985 0 .0 1 0
a. Number of cases outside the range (Q1 - 1.5*IQR, Q3 + 1.5*IQR).
OUTLIERS. From the box plot, there is one outlier for WORRY. Observation No. 96, value
= 8.
Residuals Statisticsa
Minimum Maximum Mean Std.
Deviation
N
Predicted Value -.44 1.19 .50 .432 100
Std. Predicted Value -2.168 1.591 .000 1.000 100
Standard Error of
Predicted Value
.028 .092 .051 .011 100
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Subject
Psychology