Chapter
2 Getting Acquainted with Microsoft Excel®
3 Using Statistics in Excel®
Using Statistical Functions
Entering Formulas Directly
Missing Values and ‘‘0’’ Values in Excel® Analyses
Using Excel® with Real Data
School-level Achievement Database
The STAR Classroom Observation ProtocolTM Data4
Reading and Importing Data
Additional Management Functions
Transform/compute (creating Indices)
5 Descriptive Statistics— Central Tendency
Research Applications—spuriousness
Descriptive and Inferential Statistics
The Nature of Data—scales of Measurement
Choosing the Correct Statistical Procedure for the Nature of Research Data
Descriptive Statistics—central Tendency
Using Excel® and SPSS® to Understand Central Tendency
Describing the Normal Distribution
Descriptive Statistics—using Graphical Methods
Real-world Lab I: Central Tendency
Real-world Lab I: Solutions
6 Descriptive Statistics— Variablity
Scores Based on Percentiles
Using Excel® and SPSS® to Identify Percentiles
Standard Deviation and Variance
Calculating the Variance and Standard Deviation
Sample Sd and Population Sd
Obtaining Sd from Excel® and SPSS®
Real-world Lab II: Variability
Real-world Lab II: Solutions
7 The Normal Distribution
The Nature of the Normal Curve
The Standard Normal Score: Z Score
The Z-score Table of Values
Navigating the Z-score Distribution
Creating Rules for Locating Z Scores
Working with Raw Score Distributions
Using Excel® to Create Z Scores and Cumulative Proportions
Using SPSS® to Create Z Scores
Real-world Lab Iii: the Normal Curve and Z Scores
Real-world Lab Iii: Solutions
8 The Z Distribution and Probability
Transforming a Z Score to a Raw Score
Transforming Cumulative Proportions to Z Scores
Deriving Sample Scores from Cumulative Percentages
Additional Transformations Using the Standard Normal Distribution
Using Excel® and SPSS® to Transform Scores
Determinism Versus Probability
Probability and the Normal Curve
Relationship of Z Score and Probability
‘‘inside’’ and ‘‘outside’’ Areas of the Standard Normal Distribution
From Sample Values to Sample Distributions
Real-world Lab IV: Solutions
9 The Nature of Research Design and Inferential Statistics
Types of Research Designs
Post Facto Research Designs
The Nature of Research Design
Research Design Varieties
One Sample from Many Possible Samples
Central Limit Theorem and Sampling Distributions
The Sampling Distribution and Research
The Standard Error of the Mean
‘‘Transforming’’ the Sample Mean to the Sampling Distribution
Practical Significance: Effect Size
Real-world Lab V: Solutions
10 The T Test for Single Samples
Z Versus T: Making Accommodations
Post Facto Comparative Design
Estimating the Population Standard Deviation
Biased Versus Unbiased Estimates
Type I and Type Ii Errors
Type I (alpha) Errors (α)
Type II (beta) Errors (ß)
Another Measurement of the (cohen’s D) Effect Size
Power, Effect Size, and Beta
One- and Two-tailed Tests
Choosing a One- or Two-tailed Test
Point and Interval Estimates
Calculating the Interval Estimate of the Population Mean
The Value of Confidence Intervals
Using Excel® and SPSS® with the Single-sample T Test
SPSS® and the Single-sample T Test
Excel® and the Single Sample T Test
Real-world Lab Vi: Single-sample T Test
Real-world Lab Vi: Solutions
11 Independent-samples T Test
Independent T Test: the Procedure
Creating the Sampling Distribution of Differences
The Nature of the Sampling Distribution of Differences
Calculating the Estimated Standard Error of Difference
Using Unequal Sample Sizes
Independent T-test Example
The Alternative Hypothesis
The Critical Value of Comparison
Before–after Convention with the Independent T Test
Confidence Intervals for the Independent T Test
Equal and Unequal Sample Sizes
The Assumptions for the Independent-samples T Test
The Excel® ‘‘f-test Two Sample for Variances’’ Test
The SPSS® ‘‘explore’’ Procedure for Testing the Equality of Variances
The Homogeneity of Variances Assumption for the Independent T Test
Using Excel® and SPSS® with the Independent-samples T Test
Using Excel® with the Independent T Test
Using SPSS® with the Independent T Test
Real-world Lab VII: Independent T Test
Real-world Lab VII: Solutions
1. Are Assumptions Met for the Independent T Test?
2. Calculate the Independent T Test by Hand and Perform the Hypothesis Test
3. Calculate the Effect Size and Ci0.95
4. Perform the Independent T Test with Excel® and SPSS®
5. Provide a Summary of Your Findings
A Hypothetical Example of ANOVA
The Components of Variance
Calculating the Variance: Using the Sum of Squares (SS)
Degrees of Freedom in ANOVA
Calculating Mean Squares (MS)
‘‘varieties’’ of Post Hoc Analyses
The Post Hoc Analysis Process
Tukey’s HSD (range) Test Calculation
Compare Mean Difference Values from HSD
Additional Considerations with ANOVA
A Real-world Example of Anova
Calculating SSt (SSt =25,353.49)
Calculating SSb (SSB=9782)
Calculating SSw (ss W = 15,571)
Using Excel® and SPSS® with One-way ANOVA
Excel® Procedures with One-way ANOVA
Spss® Procedures with One-way ANOVA
Nonparametric Anova Tests
Real-world Lab VIII: ANOVA
Real-world Lab VIII: Solutions
Two-way Within-subjects ANOVA
Multivariate Anova Procedures
Calculating Factorial ANOVA
Calculating the Interaction
The 2×ANOVA Summary Table
Effect Size for 2×ANOVA: Partial n2
Using Spss® to Analyze 2×ANOVA
The ‘‘plots’’ Specification
Summary Chart for 2×ANOVA Procedures
Real-world Lab IX: 2×ANOVA
Real-world Lab IX: 2×ANOVA Solutions
The Nature of Correlation
Different Measurement Values
Pearson’s Correlation Coefficient
Interpreting the Pearson’s Correlation
Assumptions for Correlation
Plotting the Correlation: the Scattergram
Strength of Correlations in Scattergrams
Using Excel® to Create Scattergrams
Using SPSS® to Create Scattergrams
The Hypothesis Test for Pearson’s r
The Comparison Table of Values
Effect Size: The Coefficient of Determination
Correlations and Sample Size
Correlation Is Not Causation
Assumptions for Correlation
Computation of Pearson’s r for the Example Data
Evaluating Pearson’s r: Hypothesis Test
Evaluating Pearson’s r: Effect Size
Correlation Using Excel® and SPSS®
Nonparametric Statistics: Spearman’s Rank-order Correlation (rs)
Variations of Spearman’s Rho Formula: Tied Ranks
Real-world Lab X: Correlation
Real-world Lab X: Solutions
The Regression Equation in ‘‘pieces’’
Interpreting and Using the Regression Equation
Effect Size of Regression
The Z-score Formula for Regression
Using the Z-score Formula for Regression
Unstandardized and Standardized Regression Coefficients
Testing the Regression Hypotheses
The Standard Error of Estimate
Explaining Variance Through Regression
Using Scattergrams to Understand the Partitioning of Variance
A Numerical Example of Partitioning the Variation
Using Excel® and SPSS® with Bivariate Regression
The Excel® Regression Output
The Spss® Regression Output
Assumptions of Bivariate Linear Regression
Curvilinear Relationships
Detecting Problems in Bivariate Linear Regression
A Real-world Example of Bivariate Linear Regression
Normal Distribution and Equal Variances Assumptions
The Regression Equation and Individual Predictor Test of Significance
Advanced Regression Procedures
Additional Considerations
Real-world Lab XI: Bivariate Linear Regression
Real-world Lab XI: Solutions
16 Introduction to Multiple Linear Regression
Same Process as Bivariate Regression
Some Differences Between Bivariate Regression and MLR
Using MLR with Categorical Data
The Unstandardized Coefficients
The Standardized Coefficients
The Squared Part Correlation
Real-world Lab XII: Multiple Linear Regression
Real-world Lab XII: MLR Solutions
17 Chi Square and Contingency Table Analysis
The Chi Square Procedure and Research Design
Expected Frequencies—equal Probability
Expected Frequencies—A Priori Assumptions
The Chi Square Test of Independence
A Fictitious Example—goodness of Fit
Frequencies Versus Proportions
Effect Size—goodness of Fit
Chi Square Test of Independence
A Fictitious Example—Test of Independence
Creating Expected Frequencies
Degrees of Freedom for the Test of Independence
The Alternate 2 × 2 Formula
Effect Size in 2 × 2 Tables: Phi
Correction for 2 × 2 Tables
Cramer’s V: Effect Size for the Chi Square Test of Independence
Repeated Measures Chi Square
Repeated Measures Chi Square Table
Using Excel® and Spss® with Chi Square
Using Excel® for Chi Square Analyses
The Excel® Count Function
The Excel® CHITEST
Function
The Excel® CHIDIST Function
Using Spss® for the Chi Square Test of Independence
Analyzing the Contingency Table Data Directly
Interpreting the Contingency Table
Real-world Lab XIII: Chi Square
Real-world Lab XIII: Solutions
Using Excel® for Chi Square Analyses
Using Spss® for Chi Square Solutions
18 Repeated Measures Procedures: Tdep and Anovaws
Independent and Dependent Samples in Research Designs
The Dependent T-test Calculation: the Long Formula
The Dependent T-test Calculation: the Difference Formula
The Tdep Ratio from the Difference Method
Using Excel® and SPSS® to Conduct the Tdep Analysis
Within-subjects ANOVA (ANOVAws)
Using SPSS® for Within-subjects Data
Appendix: Statistical Tables
Understanding Educational Statistics Using Microsoft Excel and SPSS
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