Introduction to Analysis of Variance CJ 526 Statistical Analysis in Criminal Justice

Introduction 1. Analysis of Variance (ANOVA) is an inferential statistical technique

Developer 1.Developed by Sir Ronald Fisher in the 1920’s 1.Agricultural geneticist

Relationship Between ANOVA and Independent t-Test 1. Actually, Independent t-Test is really a special case of ANOVA

Similarities With Other Parametric Inferential Procedures 1. Like all parametric inferential procedures

Purpose of ANOVA 1. Determine whether differences between the means of the groups are due to chance (sampling error)

ANOVA and Research Designs 1. Can be used with both experimental and ex post facto research designs

Experimental Research Designs 1. Researcher manipulates levels of Independent Variable to determine its effect on a Dependent Variable

Example of an Experimental Research Design Using ANOVA 1. Dr. Sophie studies the effect of different dosages of a new drug on impulsivity among children at-risk of becoming delinquent

Example of an Experimental Research Design Using ANOVA -- continued 1.Independent Variable 1.Different dosages of new drug 1.0 mg (placebo) mg mg

Ex Post Facto Research Designs 1. Researcher investigates effects of pre- existing levels of an Independent Variable on a Dependent Variable

Example of an Ex Post Facto Research Design Using ANOVA 1. Dr. Horace wants to determine whether political party affiliation has an effect on attitudes toward the death penalty

Example of an Ex Post Facto Research Design Using ANOVA -- continued 1.Independent Variable 1.Political Party Affiliation 1.Democrat 2.Independent 3.Republican

Null Hypothesis in ANOVA 1. No differences among the population means

Alternative Hypothesis in ANOVA 1. At least one population mean is different from one other population mean

Example of Pairwise Comparisons 1.Dr. Mildred wants to determine whether birth order has an effect on number of self-reported delinquent acts 2.Independent Variable 1.Birth Order 1.First Born (or only child) 2.Middle Born (if three or more children) 3.Last Born

Example of Pairwise Comparisons -- continued 3.Dependent Variable 1.Number of self-reported delinquent acts 4.Possible pairwise comparisons 1.FB ≠ MB 2.FB ≠ LB 3.MB ≠ LB 5.It is possible for this particular analysis that: 1.Any one of the pairwise comparisons could be statistically significant 2.Any two of the pairwise comparisons could be statistically significant 3.All three of the pairwise comparisons could be statistically significant

Types of ANOVA 1.One-Way ANOVA 1.One Independent Variable 2.Groups are independent

Types of ANOVA -- continued 3.Repeated-Measures ANOVA 1.Groups are dependent 2.Measure the dependent variable at more than two points in time

ANOVA and Multiple t- Tests 1. Testwise alpha

The Logic of ANOVA 1.Total variability of the DV can be analyzed by dividing it into its component parts

Components of Total Variability 1. Between-Groups 2. Measure of the overall differences between treatment conditions (groups, samples)

Within-Groups Variability 1. Measure of the amount of variability inside of each treatment condition (group, sample) 2. There will always be variability within a group

Between-Group (BG) Variability 1. Treatment Effect (TE)

Within-Group (WG) Variability 1. Individual Differences (ID) 2. Example: for race, there is more within group variability than between group variability (more genetic variation among white, or Asians, etc, than between the races

The F-Ratio 1. Obtained test statistic for ANOVA Is

The F-Ratio -- continued

1. If H 0 is true, TE = 0, F = 1

The F-Ratio -- continued

1. If H 0 is false, TE > 0, F > 1

The F-Ratio -- continued

1.F = Systematic Variability 1.divided by

Systematic Variability 1. Due to treatment

Unsystematic Variability 1. Uncontrolled or unexplained

ANOVA Vocabulary 1. Factor

Factor 1.Independent variable

Level 1. Different values of a factor

Notation for ANOVA 1.k: number of levels of a factor 1.Also the number of different samples

Degrees of Freedom 1.Between Groups 1.k - 1

F-Distribution 1. Always positive

Example 1. A police psychologist wants to determine whether caffeine has an effect on learning and memory 2. Randomly assigns 120 police officers to one of five groups:

Experimental Groups 1. 0 mg (placebo) mg mg mg mg

Example -- continued 3. Records how many “nonsense” words each police officer recalls after studying a 20-word list for 2 minutes 4. CVC, dif, zup

ANOVA Summary Table

Example of ANOVA 1.Number of Samples: 5 2.Nature of Samples:  Known:

Example of ANOVA -- continued 4.Independent Variable: caffeine 5.Dependent Variable and its Level of Measurement: number of syllables remembered—interval/ratio

Example of ANOVA -- continued 6. Target Population: 7. Appropriate Inferential Statistical Technique: one way analysis of variance 8. Null Hypothesis: no differences in memory between the groups

Example of ANOVA -- continued 9.Alternative Hypothesis: Caffeine does have an effect on memory and there will be differences among the groups 10.Decision Rule: 1.If the p-value of the obtained test statistic is less than.05, reject the null hypothesis

Example of ANOVA -- continued 11.Obtained Test Statistic: F 12.Decision: accept or reject the null hypothesis

Results 1. The results of the One-way ANOVA involving caffeine as the independent variable and number of nonsense words recalled as the dependent variable were statistically significant, F (4, 115) = 5.14, p <.01. The means and standard deviations for the five groups are contained in Table 1.

Discussion 1. It appears that the ingesting small to moderate amounts of caffeine results in better retention of nonsense syllables, but that ingesting moderate to large amounts of caffeine interferes with the ability to retain nonsense syllables

Assumptions of ANOVA 1. Observations are independent

SPSS Procedure Oneway Analyze, Compare Means, One-Way ANOVA Move DV into Depdent List Move IV into Factor Options Descriptives Homogeniety of Variance

Sample Printout: ANOVA

Sample Printout: Post Hoc Tests

SPSS Procedure One- Way Output Descriptives Levels of IV N Mean Standard Deviation Standard Error of the Mean 95% Confidence Interval Lower Bound Upper Bound

SPSS Procedure One- Way Output -- continued Test of Homogeneity of Variance ANOVA Summary Table Sum of Squares df Mean Square F Sig