Explaining Psychological StatisticsDesigned with the future practitioner in mind, this work is orgnized in an A-B-C-D format. Each chapter includes: an overview; basic statistical procedures (formulas, computations, and numerous step-wise instructions and lists); advanced (optional) material; a summary (lists of important points from each of the preceeding sections which serves as a study guide); and an exercise set. The author shows the role statistics play in research, helping motivate students as they see its applications in the clinical areas of psychological research via examples. The book emphasizes connections between seeemingly disparate statistical procedures. |
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Page 113
... Formula 4.3 , which is known as the definitional formula for SS : SS = Σ ( X ; — μ ) 2 Formula 4.3 is also called the deviational formula , because it is based directly on deviation scores . Compare this formula to the easier computational ...
... Formula 4.3 , which is known as the definitional formula for SS : SS = Σ ( X ; — μ ) 2 Formula 4.3 is also called the deviational formula , because it is based directly on deviation scores . Compare this formula to the easier computational ...
Page 266
Barry H. Cohen. Formula 4.7 provides a way of rewriting the numerator of Formula 9.6A . If we multiply both sides of Formula 4.7 by N 1 , we get SS = ( N - 1 ) s2 . This information allows us to rewrite Formula 9.6A as Formula 9.6B : - 2 ...
Barry H. Cohen. Formula 4.7 provides a way of rewriting the numerator of Formula 9.6A . If we multiply both sides of Formula 4.7 by N 1 , we get SS = ( N - 1 ) s2 . This information allows us to rewrite Formula 9.6A as Formula 9.6B : - 2 ...
Page 341
... Formula 4.9 states that s2 = SS / ( N − 1 ) = SS / df . ] The chief drawback of Formula 11.4 is that it requires calculating a deviation score for each and every raw score — which is almost as tedious as calculating all of the z scores ...
... Formula 4.9 states that s2 = SS / ( N − 1 ) = SS / df . ] The chief drawback of Formula 11.4 is that it requires calculating a deviation score for each and every raw score — which is almost as tedious as calculating all of the z scores ...
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Common terms and phrases
05 level alpha assumption average binomial distribution calculated cell Chapter chi-square column comparisons confidence interval considered correlation coefficient critical F critical value degrees of freedom denominator dependent variable described difference scores divided effect size equal equation estimate example exercise experiment experimental F ratio factor Figure Formula frequency distribution gamma graph heart rate height hypothesis testing independent interaction kurtosis linear main effect matched t-test measure median mixed design multiplied normal distribution null hypothesis null hypothesis testing number of subjects one-sample one-way ANOVA pairs Pearson's percentile perform population mean population variance predicted predictors probability randomly ranks regression reject the null researcher RM ANOVA sample means sample sizes sample variances sampling distribution skewed SSbet standard deviation statistically significant subtract Table tion total number treatment levels two-group two-tailed test two-way ANOVA Type I errors z score zero σχ