Beyond the ANOVA - Revision history

Casimir007 at 06:11, 14 November 2025

2025-11-14T06:11:50Z

Casimir007 at 06:10, 14 November 2025

2025-11-14T06:10:45Z

Admin at 15:02, 2 December 2019

2019-12-02T15:02:07Z

Kuslis002: /* ANCOVA */

2019-11-17T01:54:10Z

‎ANCOVA

Kuslis002: /* ANCOVA */

2019-11-17T01:53:35Z

‎ANCOVA

Admin: Created page with "The Analysis of Variance is a powerful statistical technique for analyzing data. But what happens when we have conditions that go beyond the ANOVA and we need more? == 2-wa..."

2019-08-29T00:30:06Z

Created page with "The Analysis of Variance is a powerful statistical technique for analyzing data. But what happens when we have conditions that go beyond the ANOVA and we need more? == 2-wa..."

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The Analysis of Variance is a powerful statistical technique for analyzing data. But what happens when we have conditions that go beyond the ANOVA and we need more?

== 2-way ANOVA ==
A two-way ANOVA is used when each participant in a study has scores on three variables: two independent variables (IV) with two or more levels and a dependent variable (DV). For example, a two-way ANOVA can be used to evaluate the effects of three different methods of math instruction on math achievement scores for boys and girls. In this example the first IV is gender which has two levels: male and female; the second IV is math instruction which has three levels: Method 1, Method 2, and a Control; the third variable is the DV-math achievement scores. The two-way ANOVA starts with an omnibus test to determine if there are any significant effects on the DV based on each IV and the interaction of the IV's. If the omnibus test indicates significance, then follow-up tests are required to specifically identify where the significant differences exist. A two-way ANOVA can be used to analyze data from experimental studies, quasi-experimental studies, and field studies.

''contributed by Helen Knudsen''
== k-way ANOVA ==

== Repeated Measures ANOVA ==

Repeated measures ANOVA, as in any ANOVA, compares the means of different groups. What a repeated measures ANOVA allows the researcher to do, however, is to compare data on the same characteristic when samples are collected at different times (i.e. within a longitudinal study).

A repeated measures ANOVA can also be used when members of a random sample are matched based upon some criteria. The data collected at various points by the matched pairs in the study can then be analyzed with this method.

Submitted by Karen A. Fildes

== ANCOVA ==

== MANOVA ==

The Multivariate Analysis of Variance, also known as the MANOVA, is used when there are multiple dependent variables and you are looking at multiple factors. The only difference between an ANOVA and a MANOVA is that the ANOVA has the limitation of only allowing for a single dependent variable while the MANOVA allows for more. For example, using the MANOVA analysis, a researcher could examine Math Achievement and Reading Achievement scores.
 
An example research question for a MANOVA would be:
Is there a significant difference in Math Achievement (computation, problem solving and numeracy) and Math Self Efficacy for students who participate in an after school treatment program for either one day per week (Program A), three days per week (Program B), or the traditional math curriculum. 

DV 1: Math Achievement 
Level 1: Computation 
Level 2: Problem Solving 
Level 3: Numeracy 

DV 2: Self Efficacy 
1V 1: Math Program 
Level 1: Program A 
Level 2: Program B 
Level 3: Comparison/Control 

''contributed by Karen Fildes''

== MANCOVA ==

← Older revision		Revision as of 06:11, 14 November 2025
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−	== Analysis of Variance (ANOVA)	+	'''Analysis of Variance (ANOVA)'''
		+	----
		+

	Analysis of variance (ANOVA) is a statistical test that is applied to compare the means of three or more groups at the same time to find out whether at least one of them significantly differs. It is a t-test extension and is normally used in education research to compare various instructional practices, grade levels, or interventions. ANOVA works by dividing the total variance in the data into two components: one between-group variance (variation between group means) and the other within-group variance (variation within each group). The F-statistic is the ratio between these two variances. When the F-value is significant and the p-value is less than 05, it is an indication that one of the group means is not equal to the others.		Analysis of variance (ANOVA) is a statistical test that is applied to compare the means of three or more groups at the same time to find out whether at least one of them significantly differs. It is a t-test extension and is normally used in education research to compare various instructional practices, grade levels, or interventions. ANOVA works by dividing the total variance in the data into two components: one between-group variance (variation between group means) and the other within-group variance (variation within each group). The F-statistic is the ratio between these two variances. When the F-value is significant and the p-value is less than 05, it is an indication that one of the group means is not equal to the others.

← Older revision		Revision as of 06:10, 14 November 2025
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	== MANCOVA ==		== MANCOVA ==
		+
		+
		+	== Analysis of Variance (ANOVA)
		+
		+	Analysis of variance (ANOVA) is a statistical test that is applied to compare the means of three or more groups at the same time to find out whether at least one of them significantly differs. It is a t-test extension and is normally used in education research to compare various instructional practices, grade levels, or interventions. ANOVA works by dividing the total variance in the data into two components: one between-group variance (variation between group means) and the other within-group variance (variation within each group). The F-statistic is the ratio between these two variances. When the F-value is significant and the p-value is less than 05, it is an indication that one of the group means is not equal to the others.
		+	An example is that an instructional leader can apply ANOVA to determine the performance of students who have been taught through three reading strategies: phonics-based, balanced literacy, and whole-language instruction. In case the results of ANOVA are significant, the results of post-hoc tests (Example is Tukey HSD or Bonferroni) indicate which particular groups differ. This is necessary since ANOVA does not inform us of the location of differences, but merely that they exist. In SPSS, do this analysis with Analyze - Compare Means - One-Way ANOVA. In the case of factorial designs with two independent variables (e.g., teaching method and grade level), a researcher can employ Two-Way ANOVA to examine interactions between variables. ANOVA assumes that there must be normality, independence, and homogeneity of variances. The violations might need other methods, including non-parametric tests (Kruskal-Wallis). Knowledge of ANOVA helps teachers to evaluate the effectiveness of programs accurately, objectively comparing various strategies to stimulate the improvement of instruction and resource distribution.
		+	==
		+
		+	----
		+
		+	''contributed by Casimir007''

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−	''~~Contribution~~ by: Britany Kuslis, WCSU Cohort 8''	+	''contributed by Britany Kuslis, WCSU Cohort 8''

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 ANCOVA Steps in SPSS
-→Select Analyze
+→ Select Analyze
 → General Linear Model
 → Univariate
 → Put the DV in the Dependent Variable box and the IV in the Fixed Factors box. Proceed to put the covariates of interest in the Covariate(s) box.
 → Click on the Options button and move the IV over to the Display Means For box
 → Click on Compare Main Effects and select Bonferroni from the Confidence interval adjustment menu to request post hoc tests.
 → Select Descriptive Statistics, Estimate of effect size and homogeneity tests from the display options.
 When writing up the results, it is common to report certain figures from the ANCOVA table.

← Older revision		Revision as of 01:53, 17 November 2019
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	== ANCOVA ==		== ANCOVA ==

		+	ANCOVA Steps in SPSS

		+	→Select Analyze
		+	→ General Linear Model
		+	→ Univariate
		+	→ Put the DV in the Dependent Variable box and the IV in the Fixed Factors box. Proceed to put the covariates of interest in the Covariate(s) box.
		+	→ Click on the Options button and move the IV over to the Display Means For box
		+	→ Click on Compare Main Effects and select Bonferroni from the Confidence interval adjustment menu to request post hoc tests.
		+	→ Select Descriptive Statistics, Estimate of effect size and homogeneity tests from the display options.
		+
		+	When writing up the results, it is common to report certain figures from the ANCOVA table.
		+	F(df between, df within)= Test Statistic, p =
		+
		+
		+	[[File:https://www.sheffield.ac.uk/polopoly_fs/1.531229!/file/MASH_ANCOVA_SPSS.pdf]]
		+
		+
		+	''Contribution by: Britany Kuslis, WCSU Cohort 8''
		+
		+
		+	Reference:
		+
		+	Rothwell, Joanne. “ANCOVA in SPSS.” www.statstutor.ac.uk, University of Sheffield, www.sheffield.ac.uk/polopoly_fs/1.531229!/file/MASH_ANCOVA_SPSS.pdf.

	== MANOVA ==		== MANOVA ==