Psychology Statistics For Dummies
Höfundur: Donncha Hanna, Martin Dempster
2.790 kr.
Lýsing:
The introduction to statistics that psychology students can't afford to be without Understanding statistics is not only a requirement, but also a necessity, for obtaining and making the most of a degree in psychology. Filled with jargon-free explanations and real-life examples, Psychology Statistics For Dummies makes the often-confusing world of statistics a lot less baffling, and provides readers with the step-by-step instructions necessary for carrying out data analysis.
Provides an easily accessible supplement to doorstop-sized cognitive psychology textbooks Includes explanations and instruction on performing statistical analysis using SPSS, the most widely used statistical packages among students For most psychology students, the study of statistics represents the most frightening and intimidating obstacle to getting a degree and achieving success. But with Psychology Statistics For Dummies in hand, it's time to face those fears safe in the knowledge that you have the resources to triumph.
Annað
- Höfundur: Donncha Hanna, Martin Dempster
- Útgáfa:1
- Útgáfudagur: 2013-01-22
- Hægt að prenta út 2 bls.
- Hægt að afrita 10 bls.
- Format:Page Fidelity
- ISBN 13: 9781119953937
- Print ISBN: 9781119952879
- ISBN 10: 1119953936
Efnisyfirlit
- Psychology Statistics For Dummies
- About the Authors
- Dedication
- Author’s Acknowledgments
- Contents at a Glance
- Table of Contents
- Introduction
- About This Book
- What You’re Not to Read
- Foolish Assumptions
- How this Book is Organised
- Icons Used in This Book
- Where to Go from Here
- Part I: Describing Data
- Chapter 1: Statistics? I Thought This Was Psychology!
- Know Your Variables
- What is SPSS?
- Descriptive Statistics
- Central tendency
- Dispersion
- Graphs
- Standardised scores
- Inferential Statistics
- Hypotheses
- Parametric and non-parametric variables
- Research Designs
- Correlational design
- Experimental design
- Independent groups design
- Repeated measures design
- Getting Started
- Chapter 2: What Type of Data Are We Dealing With?
- Understanding Discrete and Continuous Variables
- Looking at Levels of Measurement
- Measurement properties
- Types of measurement level
- Determining the Role of Variables
- Independent variables
- Dependent variables
- Covariates
- Chapter 1: Statistics? I Thought This Was Psychology!
- Chapter 3: Inputting Data, Labelling and Coding in SPSS
- Variable View Window
- Creating variable names
- Deciding on variable type
- Displaying the data: The width, decimals, columns and align headings
- Using labels
- Using values
- Dealing with missing data
- Assigning the level of measurement
- Data View Window
- Entering new data
- Creating new variables
- Sorting cases
- Recoding variables
- Output Window
- Using the output window
- Saving your output
- Variable View Window
- Defining Central Tendency
- The Mode
- Determining the mode
- Knowing the advantages and disadvantages of using the mode
- Obtaining the mode in SPSS
- The Median
- Determining the median
- Knowing the advantages and disadvantages to using the median
- Obtaining the median in SPSS
- The Mean
- Determining the mean
- Knowing the advantages and disadvantages to using the mean
- Obtaining the mean in SPSS
- Choosing between the Mode, Median and Mean
- Defining Dispersion
- The Range
- Determining the range
- Knowing the advantages and disadvantages of using the range
- Obtaining the range in SPSS
- The Interquartile Range
- Determining the interquartile range
- Knowing the advantages and disadvantages of using the interquartile range
- Obtaining the interquartile range in SPSS
- The Standard Deviation
- Defining the standard deviation
- Knowing the advantages and disadvantages of using the standard deviation
- Obtaining the standard deviation in SPSS
- Choosing between the Range, Interquartile Range and Standard Deviation
- The Histogram
- Understanding the histogram
- Obtaining a histogram in SPSS
- The Bar Chart
- Understanding the bar chart
- Obtaining a bar chart in SPSS
- The Pie Chart
- Understanding the pie chart
- Obtaining a pie chart in SPSS
- The Box and Whisker Plot
- Understanding the box and whisker plot
- Obtaining a box and whisker plot in SPSS
- Chapter 7: Understanding Probability and Inference
- Examining Statistical Inference
- Looking at the population and the sample
- Knowing the limitations of descriptive statistics
- Aiming to be 95 per cent confident
- Making Sense of Probability
- Defining probability
- Considering mutually exclusive and independent events
- Understanding conditional probability
- Knowing about odds
- Examining Statistical Inference
- Understanding Null and Alternative Hypotheses
- Testing the null hypothesis
- Defining the alternative hypothesis
- Deciding whether to accept or reject the null hypothesis
- Taking On Board Statistical Inference Errors
- Knowing about the Type I error
- Considering the Type II error
- Getting it right sometimes
- Looking at One- and Two-Tailed Hypotheses
- Using a one-tailed hypothesis
- Applying a two-tailed hypothesis
- Confidence Intervals
- Defining a 95 per cent confidence interval
- Calculating a 95 per cent confidence interval
- Obtaining a 95 per cent confidence interval in SPSS
- Understanding the Normal Distribution
- Defining the normal distribution
- Determining whether a distribution is approximately normal
- Determining Skewness
- Defining skewness
- Assessing skewness graphically
- Obtaining the skewness statistic in SPSS
- Looking at the Normal Distribution and Inferential Statistics
- Making inferences about individual scores
- Considering the sampling distribution
- Making inferences about group scores
- Knowing the Basics of Standardised Scores
- Defining standardised scores
- Calculating standardised scores
- Using Z Scores in Statistical Analyses
- Connecting Z scores and the normal distribution
- Using Z scores in inferential statistics
- Distinguishing between Effect Size and Statistical Significance
- Exploring Effect Size for Correlations
- Considering Effect Size When Comparing Differences Between Two Sets of Scores
- Obtaining an effect size for comparing differences between two sets of scores
- Interpreting an effect size for differences between two sets of scores
- Looking at Effect Size When Comparing Differences between More Than Two Sets of Scores
- Obtaining an effect size for comparing differences between more than two sets of scores
- Interpreting an effect size for differences between more than two sets of scores
- Understanding Statistical Power
- Seeing which factors influence power
- Considering power and sample size
- Chapter 12: Correlations
- Using Scatterplots to Assess Relationships
- Inspecting a scatterplot
- Drawing a scatterplot in SPSS
- Understanding the Correlation Coefficient
- Examining Shared Variance
- Using Pearson’s Correlation
- Knowing when to use Pearson’s correlation
- Performing Pearson’s correlation in SPSS
- Interpreting the output
- Writing up the results
- Using Spearman’s Correlation
- Knowing when to use Spearman’s correlation
- Performing Spearman’s correlation in SPSS
- Interpreting the output
- Writing up the results
- Using Kendall’s Correlation
- Performing Kendall’s correlation in SPSS
- Interpreting the output
- Writing up the results
- Using Partial Correlation
- Performing partial correlation in SPSS
- Interpreting the output
- Writing up the results
- Using Scatterplots to Assess Relationships
- Getting to Grips with the Basics of Regression
- Adding a regression line
- Working out residuals
- Using the regression equation
- Using Simple Regression
- Performing simple regression in SPSS
- Interpreting the output
- Writing up the results
- Working with Multiple Variables: Multiple Regression
- Performing multiple regression in SPSS
- Interpreting the output
- Writing up the results
- Checking Assumptions of Regression
- Normally distributed residuals
- Linearity
- Outliers
- Multicollinearity
- Homoscedasticity
- Type of data
- Summarising Results in a Contingency Table
- Observed frequencies in contingency tables
- Percentaging a contingency table
- Obtaining contingency tables in SPSS
- Calculating Chi-Square
- Expected frequencies
- Calculating chi-square
- Obtaining chi-square in SPSS
- Interpreting the output from chi-square in SPSS
- Writing up the results of a chi-square analysis
- Understanding the assumptions of chi-square analysis
- Measuring the Strength of Association between Two Variables
- Looking at the odds ratio
- Phi and Cramer’s V Coefficients
- Obtaining odds ratio, phi coefficient and Cramer’s V in SPSS
- Using the McNemar Test
- Calculating the McNemar test
- Obtaining a McNemar test in SPSS
- Chapter 15: Independent t-tests and Mann–Whitney Tests
- Understanding Independent Groups Design
- The Independent t-test
- Performing the independent t-test in SPSS
- Interpreting the output
- Writing up the results
- Considering assumptions
- Mann-Whitney test
- Performing the Mann–Whitney test in SPSS
- Interpreting the output
- Writing up the results
- Considering assumptions
- One-Way Between-Groups ANOVA
- Seeing how ANOVA works
- Calculating a one-way between-groups ANOVA
- Obtaining a one-way between-groups ANOVA in SPSS
- Interpreting the SPSS output for a one-way between-groups ANOVA
- Writing up the results of a one-way between-groups ANOVA
- Considering assumptions of a one-way between-groups ANOVA
- Two-Way Between-Groups ANOVA
- Understanding main effects and interactions
- Obtaining a two-way between-groups ANOVA in SPSS
- Interpreting the SPSS output for a two-way between-groups ANOVA
- Writing up the results of a two-way between-groups ANOVA
- Considering assumptions of a two-way between-groups ANOVA
- Kruskal–Wallis Test
- Obtaining a Kruskal–Wallis test in SPSS
- Interpreting the SPSS output for a Kruskal–Wallis test
- Writing up the results of a Kruskal–Wallis test
- Considering assumptions of a Kruskal–Wallis test
- Post Hoc Tests for Independent Groups Designs
- Multiplicity
- Choosing a post hoc test
- Obtaining a Tukey HSD post hoc test in SPSS
- Interpreting the SPSS output for a Tukey HSD post hoc test
- Writing up the results of a post hoc Tukey HSD test
- Planned Comparisons for Independent Groups Designs
- Choosing a planned comparison
- Obtaining a Dunnett test in SPSS
- Interpreting the SPSS output for a Dunnett test
- Writing up the results of a Dunnett test
- Chapter 18: Paired t-tests and Wilcoxon Tests
- Understanding Repeated Measures Design
- Paired t-test
- Performing a paired t-test in SPSS
- Interpreting the output
- Writing up the results
- Assumptions
- The Wilcoxon Test
- Performing the Wilcoxon test in SPSS
- Interpreting the output
- Writing up the results
- One-Way Within-Groups ANOVA
- Knowing how ANOVA works
- The example
- Obtaining a one-way within-groups ANOVA in SPSS
- Interpreting the SPSS output for a one-way within-groups ANOVA
- Writing up the results of a one-way within-groups ANOVA
- Assumptions of a one-way within-groups ANOVA
- Two-Way Within-Groups ANOVA
- Main effects and interactions
- Obtaining a two-way within-groups ANOVA in SPSS
- Interpreting the SPSS output for a two-way within-groups ANOVA
- Interpreting the interaction plot from a two-way within-groups ANOVA
- Writing up the results of a two-way within-groups ANOVA
- Assumptions of a two-way within-groups ANOVA
- The Friedman Test
- Obtaining a Friedman test in SPSS
- Interpreting the SPSS output for a Friedman test
- Writing up the results of a Friedman test
- Assumptions of the Friedman test
- Why do you need to use post hoc tests and planned comparisons?
- Why should you not use t-tests?
- What is the difference between post hoc tests and planned comparisons?
- Post Hoc Tests for Repeated Measures Designs
- The example
- Choosing a post hoc test
- Obtaining a post-hoc test for a within-groups ANOVA in SPSS
- Interpreting the SPSS output for a post-hoc test
- Writing up the results of a post hoc test
- Planned Comparisons for Within Groups Designs
- The example
- Choosing a planned comparison
- Obtaining a simple planned contrast in SPSS
- Interpreting the SPSS output for planned comparison tests
- Writing up the results of planned contrasts
- Examining Differences between Conditions: The Bonferroni Correction
- Getting to Grips with Mixed ANOVA
- The example
- Main Effects and Interactions
- Performing the ANOVA in SPSS
- Interpreting the SPSS output for a two-way mixed ANOVA
- Writing up the results of a two-way mixed ANOVA
- Assumptions
- Chapter 22: Ten Pieces of Good Advice for Inferential Testing
- Statistical Significance Is Not the Same as Practical Significance
- Fail to Prepare, Prepare to Fail
- Don’t Go Fishing for a Significant Result
- Check Your Assumptions
- My p Is Bigger Than Your p
- Differences and Relationships Are Not Opposing Trends
- Where Did My Post-hoc Tests Go?
- Categorising Continuous Data
- Be Consistent
- Get Help!
- Chapter 23: Ten Tips for Writing Your Results Section
- Reporting the p-value
- Reporting Other Figures
- Don’t Forget About the Descriptive Statistics
- Do Not Overuse the Mean
- Report Effect Sizes and Direction of Effects
- The Case of the Missing Participants
- Be Careful With Your Language
- Beware Correlations and Causality
- Make Sure to Answer Your Own Question
- Add Some Structure
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