Table of Contents and Learning Objectives

Table of Contents

Main Body

  1. Chapter 1 Why We Need Statistics and Displaying Data Using Tables and Graphs
  2. Chapter 2 Central Tendency and Variability
  3. Chapter 3 Z-scores and the Normal Curve
  4. Chapter 4 Probability, Inferential Statistics, and Hypothesis Testing
  5. Chapter 5 Single Sample Z-test and t-test
  6. Chapter 6 Dependent t-test
  7. Chapter 7 Independent Means t-test
  8. Chapter 8 Analysis of Variance, Planned Contrasts and Posthoc Tests
  9. Chapter 9 Factorial ANOVA and Interaction Effects
  10. Chapter 10 Correlation and Regression
  11. Chapter 11 Beyond Hypothesis Testing
  12. Afterword

Homework Assignments

Appendices

Learning Objectives

Chapter 1 Why We Need Statistics and Displaying Data Using Tables and Graphs

  • articulate the purpose of a course introducing statistical principles and techniques
  • supply examples of situations in which data analysis techniques may be necessary
  • define descriptive and inferential statistics, variable, value, and score
  • distinguish between two levels of measurement and identify the appropriate techniques for summarizing different types of data
  • generate frequency tables
  • graph a dataset using a histogram, bar graph, or pie chart
  • describe a distribution shape in terms of peaks and symmetry

Chapter 2 Central Tendency and Variability

  • define and determine mean, median, and mode, as three options to determine central tendency
  • distinguish among the measures of central tendency and the circumstances under which each is suitable
  • define and determine variance and standard deviation, as two options to determine variability
  • interpret standard deviation

Chapter 3 Z-scores and the Normal Curve

  • transform scores in any numeric dataset, using any scale, into the standard metric of Z-scores
  • interpret Z-scores and apply them for comparison of scores within and between datasets, including data measured on different scales
  • define and characterize the normal curve model
  • associate Z-scores with areas under the normal curve
  • define percentiles and determine Z-scores and raw scores that form the border of percentiles using the normal curve model

Chapter 4 Probability, Inferential Statistics, and Hypothesis Testing

  • determine simple probabilities
  • appreciate the importance of probability and ubiquity of human failings in the realm of probability
  • connect probability to percentiles, areas under the normal curve, and the logic of inferential statistics such as hypothesis testing
  • define and distinguish between population and sample
  • articulate the central tendency theorem and describe its implications for the normality assumption in inferential statistics
  • outline and apply the steps of hypothesis testing

Chapter 5 Single Sample Z-test and t-test

  • define and identify Type I and Type II errors
  • define and characterize the distribution of means as compared to the distribution of individuals
  • determine the mean and standard deviation of the distribution of means based on the characteristics of the distribution of individuals
  • conduct a hypothesis test using the single sample Z-test
  • define, determine, and interpret a p-value
  • articulate a conclusion in plain language from an test of statistical significance
  • define and determine degrees of freedom
  • articulate the logic behind the sample size correction for sample-based estimates of variance
  • describe the difference between t-distribution shapes with varying degrees of freedom
  • conduct a hypothesis test using the single sample t-test
  • identify scenarios in which a single sample Z-test or t-test is appropriate

Chapter 6 Dependent t-test

  • identify and describe repeated measures and matched pairs research designs
  • conduct a hypothesis test using the dependent means t-test
  • identify scenarios in which a dependent means t-test is appropriate

Chapter 7 Independent Means t-test

  • identify and describe classical experimental research designs
  • identify the (normal curve and homoscedasticity) assumptions behind the independent means t-test
  • conduct a hypothesis test using the independent means t-test
  • identify scenarios in which an independent means t-test is appropriate

Chapter 8 Analysis of Variance, Planned Contrasts and Posthoc Tests

  • define partitioning of variance and apply the concept to one-way Analysis of Variance
  • define and identify factors and levels in research designs
  • use graphing techniques to visualize data from a research design using more than 2 levels in a factor
  • conduct a hypothesis test using one-way Analysis of Variance
  • articulate reasons for conducting planned contrasts or post-hoc tests following ANOVA
  • define experimentwise alpha level and articulate ways in which Bonferroni and Scheffé corrections address inflated risk of Type I error
  • outline the procedure for conducting planned contrasts with Bonferroni correction
  • outline the procedure for conducting posthoc tests with Scheffé correction
  • identify scenarios in which a one-way ANOVA is appropriate

Chapter 9 Factorial ANOVA and Interaction Effects

  • apply  the concept of partitioning of variance to two-way Analysis of Variance
  • describe factorial analysis and articulate its benefits and pitfalls
  • describe research designs using ___ X ___ factor and level summaries
  • conduct a hypothesis test using two-way Analysis of Variance
  • identify scenarios in which a two-way ANOVA is appropriate
  • identify and interpret main effects
  • identify and interpret interactions

Chapter 10 Correlation and Regression

  • define correlation and regression
  • detect and describe linear correlation patterns using scatterplots
  • define partitioning of covariance
  • conduct a hypothesis test using correlation
  • find the proportion of variance explained by a correlation
  • identify scenarios in which a correlation is appropriate
  • create a predictive model using a simple regression line
  • articulate limits to accuracy and usefulness of regression models

Chapter 11 Beyond Hypothesis Testing

  • define effect size, power, and confidence intervals
  • articulate the importance of effect size and power analyses
  • find and interpret Cohen’s d for a single-sample Z-test scenario
  • identify the two major determinants of statistical power
  • estimate and interpret power for a single-sample Z-test scenario
  • construct confidence intervals for a single-sample Z-test scenario
  • articulate similarities and differences between hypothesis testing and confidence interval procedures

License

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Beginner Statistics for Psychology Copyright © 2021 by Nicole Vittoz is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.