tailieunhanh - Ebook Introduction to statistics in psychology (5th edition): Part 1

(BQ) Part 1 book "Introduction to statistics in psychology" has contents: Reporting significance levels succinctly, One-tailed versus two-tai led significance testing, analysis of variance for correlated scores or repeated measures,.and other contents. | CHAPTER 23 Multiple comparisons in ANOVA Just where do the differences lie? Overview Generally speaking, analyses of variance are relatively easy to interpret if the independent variables all have just two different values. Interpretation becomes difficult with greater numbers of values of the independent variables. This is because the analysis does not stipulate which means are significantly different from each other. If there are only two values of each independent variable, then statistical significance means that those two values are significantly different. Multiple comparison tests are available to indicate just where the differences lie. These multiple comparison tests have built-in adjustment for the numbers of comparisons being made. Hence they are generally to be preferred over multiple comparisons using the t-test. It is very difficult to know which multiple comparison tests are the most appropriate for any particular data or purpose. Consequently, it is reasonable advice that several different tests should be used. The only problem that arises is when the different tests yield different conclusions. Some multiple comparison tests may be applied whether or not the ANOVA itself is statistically significant. Preparation You will need a working knowledge of Chapters 19, 20 and 21 on the analysis of variance. Chapter 14 introduces the problem of multiple comparisons in the context of partitioning chi-square tables. CHAPTER 23 MULTIPLE COMPARISONS IN ANOVA 277 Introduction When in research there are more than two levels of an independent variable it is not always obvious where the differences between conditions lie. There is no problem when you have only two groups of scores to compare in a one-way or a 2 × 2 ANOVA. However, if there are three or more different levels of any independent variable the interpretation problems multiply. Take, for example, Table of means for a one-way analysis of variance. Although the analysis of variance for the

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• Statistics in psychology
• Reporting significance levels succinctly
• Descriptive statistics
• Descriptive statistics
• Descriptive statistics
• Methods in psychology
• The error statistical perspective
• The new statistics
• Bayesian statistics
• Philosophy of statistics
• Aviation psychology and human factors
• Aviation psychology
• Models and psychological constructs
• Human performance models
• Research methods and statistics
• The design of aviation systems
• Statistics without maths
• Research design
• Hypothesis testing
• Statistical significance
• Issues of significance
• Analysis of differences between
• Regression analysis
• Introduction to factor analysis
• Effects of a covariate
• More groups partialling
• empirical method
• operational definitions
• experimental method
• Observational methods
• establishment paradigm
• discrete scales
• Multiple correlation
• Path analysis
• Binomial logistic regression
• Multiple regression
• Multivariate analysis
• Nominal techniques
• Lecture Business statistics
• Bài giảng Thống kê doanh nghiệp
• Business statistics
• Descriptive statistics tabular
• Descriptive statistics graphical presentations
• Summarizing quantitative data
• The essentials of statistics
• Inferential statistics
• Descriptive statistics percentages
• Central tendency
• Numerical measures
• Exploratory data analysis
• Statistics for management and economics
• Graphical descriptive techniques I
• Graphical descriptive techniques II
• Numerical descriptive techniques
• Continuous probability distributions
• Sampling distributions
• Essentials of modern business statistics
• Modern business statistics
• Graphical presentations
• Probability and statistics for engineers
• Probability and statistics for scientists
• Probability and statistics
• Elements of probability
• Parameter estimation
• Business statistics in practice
• Data sources
• Statistical inference
• Illustrate sampling
• Graphical methods
• Tabular methods
• Graphically summarizing qualitative data
• Numerical methods
• Describing central tendency
• Measures of variation
• Sample spaces
• Elementary probability rules
• Compute conditional probabilities
• Discrete random variables
• Discrete probability distributions
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• Continuous random variables
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• Confidence intervals
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• Sampling distribution
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• Chi square tests
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• Multinomial experiment
• Simple linear regression analysis
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• Least squares point estimate
• Multiple regression model
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• Statistical process control
• Causes of variation
• Control charts
• Nonparametric methods
• Hypothesis test