
Nguồn ngoài
Behavioral Statistics
This text was created to provide students with a solid foundation in statistics as used in psychological, sociological, and behavioral research. Students will develop a useable understanding of research design, the organization of data, measures of central tendency and variability, central tendency theory, descriptive and inferential statistics, parametric and nonparametric tests, and basic test assumptions.
- Tác giả
- Michelle Oja
- Nhà xuất bản
- Taft College
- Nguồn
- LibreTexts
- Giấy phép
- Mixed
- Ngôn ngữ
- EN
- Danh mục
- Khoa học cơ bản
Mục lục· 172 mục
- 01: Introduction to Behavioral Statistics
- 1.01: Why are you taking this course
- 1.02: Making Meaning of Numbers
- 1.03: How Do You Know
- 1.3.01: The Scientific Method
- 1.04: Types of Data
- 1.4.01: IV and DV
- 1.4.02: Types of Variables
- 1.4.03: Scales of Measurement
- 1.05: Populations and Samples
- 1.06: Collecting Data- More Practice with Populations and Samples
- 1.07: Research shows that...
- 1.08: Learning (Statistics)
- 02: What Do Data Look Like (Graphs)
- 2.01: Introduction to Looking at Data
- 2.02: Frequency Tables
- 2.03: APA Style Tables
- 2.04: Graphing Qualitative Variables- Bar Graphs
- 2.05: Graphing Qualitative Variables- Pie Charts
- 2.06: Graphing Quantitative Variables
- 2.07: Skew and Kurtosis
- 2.08: Graphing Quantitative Data- Line Graphs
- 2.09: Graphing Quantitative Data- Histograms
- 2.10: Graphing Quantitative Data- Means Plot
- 2.11: Graphing Quantitative Data- Boxplots
- 2.12: Graphing Quantitative Data- Scatterplots
- 2.13: Summary and Some Honesty
- 2.14: APA Style Charts
- 03: Measures of Central Tendency
- 3.01: Introduction to Measures of Central Tendency
- 3.02: Math Refresher
- 3.2.01: Decimals
- 3.03: Mode
- 3.04: Median
- 3.05: Mean
- 3.06: Interpreting All Three
- 3.07: Summary of Measures of Central Tendency
- 04: Measures of Variability
- 4.01: Introduction to Measures of Variability
- 4.02: Introduction to Standard Deviation Calculations
- 4.03: Practice SD on Wait Times
- 4.04: Interpreting Standard Deviations
- 4.05: Fractions Decimals and Percents Oh My
- 4.06: Putting All of the Measures Together
- 05: Distributions
- 5.01: Introduction to Distributions
- 5.02: Introduction to Probability
- 5.03: The Binomial Distribution
- 5.04: The Law of Large Numbers
- 5.05: Three Important Distributions Together
- 5.06: Sampling Distributions and the Central Limit Theorem
- 5.07: Putting it All Together
- 06: Using z
- 6.01: Introduction to z-scores
- 6.02: Calculating z-scores
- 6.03: Practice z-score Calculation on IQ Scores
- 6.04: Introduction to the z table
- 6.05: Practice Using the z Table
- 6.06: Predicting Amounts
- 6.07: Summary of z Scores
- 07: APA Style
- 7.01: APA and APA Style
- 7.02: APA Style Resources
- 7.03: General Paper Format
- 7.04: Formatting by Section
- 7.05: Tables and Figures
- 08: Inferential Statistics and Hypothesis Testing
- 8.01: Growth Mindset
- 8.02: Samples and Populations Refresher
- 8.03: Can Samples Predict Populations
- 8.04: Descriptive versus Inferential Statistics
- 8.05: The Research Hypothesis and the Null Hypothesis
- 8.06: Null Hypothesis Significance Testing
- 8.07: Critical Values
- 8.7.01: Rejection Region Visualization
- 8.08: Practice with p-values and NHST
- 8.09: Steps of the Hypothesis Testing Process
- 8.10: The Two Errors in Null Hypothesis Significance Testing
- 8.11: Power and Sample Size
- 8.12: Views of NHST
- 09: One Sample t-test
- 9.01: Introduction to One-Sample t-tests
- 9.02: One-Sample t-test Calculations
- 9.03: Reporting Results
- 9.04: Practice One-Sample t-tests on Mindset Scores
- 9.05: Confidence Intervals
- 10: Independent Samples t-test
- 10.01: Introduction to Independent Sample t-tests
- 10.02: Independent Samples t-test Equation
- 10.03: Hypotheses with Two Samples
- 10.04: Practice Independent t-test on Movies and Mood
- 10.05: Practice Independent t-test on Growth Mindset
- 10.06: When to NOT use the Independent Samples t-test
- 10.07: Non-Parametric Independent Sample t-Test
- 11: Dependent Samples t-test
- 11.01: Introduction to Dependent Samples
- 11.02: Dependent Sample t-test Calculations
- 11.03: Practice Dependent t-test on Job Satisfaction
- 11.04: Practice Dependent t-test on Changes in Mindset
- 11.05: Non-Parametric Analysis of Dependent Samples
- 11.06: Choosing Which Statistic- t-test Edition
- 12: BG ANOVA
- 12.01: Why ANOVA
- 12.1.01: Observing and Interpreting Variability
- 12.1.02: Ratio of Variability
- 12.02: Introduction to ANOVA's Sum of Squares
- 12.03: Summary of ANOVA Summary Table
- 12.04: Hypotheses in ANOVA
- 12.05: Practice BG ANOVA on Job Applicants
- 12.06: Introduction to Pairwise Comparisons
- 12.07: Pairwise Comparison Post Hoc Tests
- 12.08: Practice BG ANOVA and Pairwise Comparisons on Mindset Data
- 12.09: ANOVA versus t-test
- 12.10: Non-Parametric Analysis Between Multiple Groups
- 13: RM ANOVA
- 13.01: Introduction to Repeated Measures ANOVA
- 13.02: RM ANOVA Summary Table
- 13.03: Repeated Measures ANOVA Sum of Squares Formulas
- 13.04: Practice RM ANOVA Summary Table on Typing Speed
- 13.05: Practice RM ANOVA on Mindset Scores
- 13.06: Non-Parametric RM ANOVA
- 14: Factorial ANOVA (Two-Way)
- 14.01: Introduction to Factorial Designs
- 14.02: Factorial Notations and Square Tables
- 14.03: Introduction to Main Effects and Interactions
- 14.04: Example with Main Effects and Interactions
- 14.05: Graphing Main Effects and Interactions
- 14.06: Interpreting Main Effects and Interactions in Graphs
- 14.07: Do Main Effects Matter
- 14.08: Beyond 2x2 in Graphs
- 14.09: Two-Way ANOVA Summary Table
- 14.9.01: Calculating Sum of Squares for the Factorial ANOVA Summary Table
- 14.10: When Should You Conduct Post-Hoc Pairwise Comparisons
- 14.11: Practice Factorial on Attention
- 14.12: Practice Factorial on Mindset Scores
- 14.13: Choosing the Correct Analysis- Mean Comparison Edition
- 15: Correlations
- 15.01: Refresh to Prepare
- 15.02: What do Two Quantitative Variables Look Like
- 15.03: Introduction to Pearsons r
- 15.04: Correlation versus Causation
- 15.4.01: Correlation versus Causation in Graphs
- 15.05: Strength Direction and Linearity
- 15.06: Hypotheses
- 15.07: Correlation Formula- Covariance Divided by Variability
- 15.08: Practice Correlation on Anxiety and Depression
- 15.09: Practice Correlation on Nutrition
- 15.10: Alternatives to Pearson's Correlation
- 15.11: Final Considerations
- 16: Regression
- 16.01: Introduction- Line of Best Fit
- 16.02: Regression Line Equation
- 16.03: Using Linear Equations
- 16.04: Hypothesis Testing- Slope to ANOVAs
- 16.05: Practice Regression on Health and Happiness
- 16.06: Practice Regression on Nutrition
- 16.07: Multiple Regression
- 17: Chi-Square
- 17.01: Introduction to Chi-Square
- 17.02: Assumptions of the Test(s)
- 17.03: Introduction to Goodness-of-Fit
- 17.04: Interpretation of the Goodness-of-Fit Test
- 17.05: Goodness of Fit Formula
- 17.06: Practice Goodness of Fit on Pineapple on Pizza
- 17.07: Introduction to Test of Independence
- 17.08: Practice Test of Independence on College Sports
- 17.09: Practice Chi-Square on Fast Food Meals
- 17.10: Phi Correlation is Not a Correlation
- 17.11: Choosing the Correct Test- Chi-Square Edition
- 18: Wrap Up
- 18.01: Choosing the Test
- 18.02: Why did you take this class