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# MANOVA SPSS RESEARCH (Statistics Project Sample)

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The paper discusses a study using a one-way Multivariate Analysis of Variance (MANOVA) to explore whether there are significant differences in two dependent variables—fear of asking for help and motivation score—across three types of statistics classes: experimental, traditional, and flipped. The study seeks to understand how the format of a statistics class influences students' levels of motivation and their fear of asking for help.
The null hypothesis states that there is no significant difference in the fear of asking for help and motivation score based on class type. MANOVA is chosen as it allows the simultaneous analysis of multiple dependent variables (fear and motivation scores) while comparing across different class types.
Results from SPSS (Statistical Package for the Social Sciences) show that the flipped classroom had the highest mean scores for both fear of asking for help and motivation compared to experimental and traditional classes. Statistical tests such as Levene's Test and Box's M Test were used to ensure the equality of variances and covariance across groups.
The multivariate tests, including Pillai's Trace, Wilks' Lambda, and Roy's Largest Root, indicated that while there was no significant difference in fear of asking for help across class types, there was a statistically significant difference in motivation scores. Specifically, the flipped class had a higher motivation score compared to the experimental and traditional classes, with a notable mean difference between flipped and experimental class types.
The paper concludes that while the class format doesn't significantly impact students' fear of asking for help, it does influence their motivation levels, particularly with the flipped classroom showing better outcomes. Thus, the null hypothesis is rejected for the motivation score but retained for the fear of asking for help. source..

Content:

Hilary F. Hemmatipour (BS, MS)
Department of Psychology, Walden University
RSCH-8260
Cameron R. John (Ph.D.)
July 30th, 2023
MANOVA in SPSS
Research Question: Is any difference in fear of asking for help and motivation score across stat class types?
Null Hypothesis: Ho: There is no difference in fear of asking for help and motivation score across stat class type factor.
The most appropriate research design that aligns with our research question is a one-way multivariate analysis of variance (MANOVA), used to determine whether there is any significant difference between the independent variable on more than one dependent variable (continuous or scale variables). This test type differs from one-way ANOVA, which measures only one continuous dependent variable. It is better to understand type 1 errors when testing for significance using type 1 errors (Statistics, 2018). This means rejecting the null hypothesis when it must not be rejected. This includes believing that you have found something significant when nothing significant has occurred. The dependent variables include; fear of asking for help and motivation score, which should be measured at the ratio or interval level. In addition, the independent variable is Stat class type, categorized as experimental, traditional, and flipped. According to the level and measurement of the independent variable in MANOVA, it is assumed that Stat class type is measured as a categorical variable (Walden University, LLC. (Producer), 2017q).
The SPSS output is displayed in the appendix section of the paper. Table 2 shows the descriptive statistics section shows the mean for fear of asking for help, where flipped stat class type is slightly higher than the experimental and traditional classes. This indicates that the flipped class had better confidence in asking for statistics help than the traditional and experimental classes. Still, on the dependent variable motivation score, students in the flipped class also had the highest mean value. In contrast, the traditional class had a slightly higher mean score than the experimental stat class (Walden University, LLC. (Producer), 2017o). Consequently, Levene's test of equity of variance is used to investigate whether or not the analysis of variance between independent variables are same, also termed homogeneity of variance, which indicates equal variance between groups. On the other hand, Box’s M Test is used to understand the equality of covariance derived between the groups. It tests the significance at α = .005 since the test is measured highly sensitively (Emerson, 2018). Box's Test results show a p-value higher than the alpha level. Thus, we assume some equality of variance. For the multivariate tests in Table 4, it is always considered the independent variable Stat class type based on the Pillar's trace or Wilks' Lamba. Pillar's trace is the least sensitive to violating the assumptions of covariate measures, that is, rejecting the null hypothesis when it is not true (Stukalin & Einat, 2019).
From our study here, Pillai's Trace is slightly higher above the threshold of 0.05, with an F (2.218) and P (0.066). Looking at the significance of Wilk's Lamba is slightly lower than Pillai's Trace, F (2.225) and P (0.65). Since there is no significance for Pillai's Trace and Wilk's Lamba, it can be best to look at Roy's Largest Root, F (4.403) and P (0.013), which is statistically significant (Emerson, 2018). From the multivariate test, there is some statistically significant difference to reject the null hypothesis that the Stat class types are on the same level of performance based on fear of asking for help and motivation score. There is no statistically significant difference between fear of asking for help and Stat class type, but the motivation score is significant. Moreover, partial et square (η2) displays the assumptions of covariance and is also used as the effect size for the multiple analysis of variance model (Walden University, LLC. (Producer), 2017o). As illustrated in the table, there is a partial (η2 = 0.024) between class type and motivation score. Thus, this indicates a 2% variability between class type (experimental, flipped, and traditional) and motivation score. In the pairwise comparison displayed in Table 9, there is no significance between Stat class type and fear of asking for help. Also, from these multiple comparisons, the experimental and flipped class is significant (p=0.018) for the motivation score (Emerson, 2018). This reveals a slight difference of 8.4390 between the experimental and flipped classes. Hence, there are differences in fear of asking for help and motivation scores across stat class types. To conclude, we fail to reject the null hypothesis.
In summary, statistical techniques are vital in social change science. Social statistics use statistics to study and understand social environments and human behavior. Statistical methods are compulsory for every applied social research effort to attain a non-questionable conclusion. It seems that scholar depends heavily on classical statistical techniques though they could aid in using advanced and new methods.
References
Emerson, R. W. (2018). MANOVA (Multivariate Analysis of Variance): An Expanded Form of the ANOVA (Analysis of Variance). Journal of visual impairment & blindness, 112(1), 125-127.
Statistics, L. (2018). One-way Repeated Measures MANOVA in SPSS statistics. Statistical tutorials and software guides.
Stukalin, Y., & Einat, H. (2019). Analyzing test batteries in animal models of psychopathology with multivariate analysis of variance (MANOVA): one possible approach to increase external validity. Pharmacology Biochemistry and Behavior, 178, 51-55.
Walden University, LLC. (Producer). (2017o). Introduction to multivariate analysis of variance [Video file]. Baltimore, MD: Author.
Walden University, LLC. (Producer). (2017q). MANOVA [Video file]. Baltimore, MD: Author.
Appendix
Table 1: Between-Subjects Factors
Between-Subjects Factors
Value Label
N
Stats Class Type
1
Experimental
46
2
Flipped
120
3
Traditional
169
Table 2: Descriptive Statistics
Descriptive Statistics
Stats Class Type
Mean
Std. Deviation
N
Fear of Asking for Help
Experimental
.4130
.49782
46
Flipped
.4583
.50035
120
Traditional
.4142
.49405
169
Total
.4299
.49580
335
Motivation Score
Experimental
37.4994
17.48708
46
Flipped
45.9385
16.63288
120
Traditional
42.2273
18.21095
169
Total
42.9075
17.72409
335
Table 3: Box Test of Equality of Covariance Matrices
Box's Test of Equality of Covariance Matrices
Box's M
2.358
F
.388
df1
6
df2
159952.781
Sig.
.887
Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.
a. Design: Intercept + ClassType
Table 4: Multivariate Tests
Multivariate Tests
Effect
Value
F
Hypothesis df
Error df
Sig.
Partial Eta Squared
Intercept
Pillai's Trace
.833
825.322b
2.000
331.000
<.001
.833
Wilks' Lambda
.167
825.322b
2.000
331.000
<.001
.833
Hotelling's Trace
4.987
825.322b
2.000
331.000
<.001
.833
Roy's Largest Root
4.987
825.322b
2.000
331.000
<.001
.833
ClassType
Pillai's Trace
.026
2.218
4.000
664.000
.066
.013
Wilks' Lambda
.974
2.225b
4.000
662.000
.065
.013
Hotelling's Trace
.027
2.232
4.000
660.000
.064
.013
Roy's Largest Root
.027
4.403c
2.000
332.000
.013
.026
a. Design: Intercept + ClassType
b. Exact statistic
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
Table 5: Levene’s Test of Equality of Error Variance
Levene's Test of Equality of Error Variances
Levene Statistic
df1
df2
Sig.
Fear of Asking for Help
Based on Mean
.938
2
332
.392
Based on Median
.307
2
332
.736
Based on Median and with adjusted df
.307
2
331.954
.736
Based on trimmed mean
.938
2
332
.392
Motivation Score
Based on Mean
1.856
2
332
.158
Based on Median
1.903
...

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