Psychology Statistics For Dummies

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When working with psychology statistics you can classify variables according to their measurement properties. When you record variables on a data sheet, you usually record the values on the variables as numbers, because this makes statistical analysis easier. However, the numbers can have different measurement properties and these determine what types of analyses you can do with these numbers. The variable’s level of measurement is a classification system that tells you what measurement properties the values of a variable have. This textbook is as culturally inclusive as any statistics textbook. This is an area where the professor will want to supplement if they espouse the APA Guidelines for the Undergraduate Psychology Major 2.0. I recommend Kenneth Keith's book Culture Across the Curriculum as a starting point. The majority of the content in the text seems accurate. There is an error in the effect size formula for Chapter 9 - it shows the calculation for t instead of d.

Psychology Statistics For Dummies | Wiley Psychology Statistics For Dummies | Wiley

Overall the text seems consistent in terms of terminology and framework. There are some consistency issues between the chapters. In particular, some of the formulas can be difficult to read in how they are formatted - Chapters 6, 7 and 8 the formulas that include the standard error formula look odd (the fraction in the denominator), but in the other chapters the formulas look fine. The X-bar line is too long when showing the sample mean throughout the text.In Chapter 2, there are graphs with iMacs from the 1990s. I've seen this graphic in other texts and it always throws me off because most students were not yet born when these computers were out. Also, I've found that psych stats books don't actually have examples from psychology. This book is no exception. Very few examples, if any, that I came across were from the psychological sciences. Data measured at the ordinal level: The range and interquartile range are appropriate. The interquartile range is usually preferable, as it is more informative than the range. Correlate that - get the lowdown on common procedures for defining relationships between variables, including linear regressions, associations between categorical data and more Level of measurement: You need to distinguish between three levels of measurement (nominal, ordinal, and interval/ratio) when choosing a measure of central tendency. The is not indexed for a pdf reader making unusable for a course. Adding bookmarks to each chapter and chapter sub-sections would make the text much more usable.

Statistics An Introduction to Psychological Statistics

I think this text is written at an appropriate level for the target audience and appropriate context is introduced when covering technical terminology. I particularly liked the visual of the distribution balancing on a triangle to show symmetrical and asymmetrical distributions (Chapter 3). The standard deviation (often abbreviated to Std. Dev. or SD) is the average deviation of scores in your data set from their mean score for a particular variable. The mean score is the average of scores on a variable. The standard deviation indicates the extent to which the scores on a variable deviate from the mean score. Pros include: End-of-chapter exercises with answers to odd numbered problems, the most common measures of effect size are used (e.g., Uses Cohen’s d for z and t-tests, eta squared for ANOVA, Cramer’s V for chi-square), focus in correlations chapter is on Pearson correlation rather than Spearman (or other types of correlations) which is appropriate given that Pearson correlations are the type overwhelmingly used in psychological research. The hypotheses are written out in words (as you would in a psychological research report) and not just mathematical symbols. Analyse by inference - master key methods in inferential statistics, including techniques for analysing independent groups designs and repeated-measures research designs

The text can be divided into smaller sections as written. It would be hard to selectively chose sections to cover and not others because of the comprehensive nature of the material. However, these chapters can be selectively used if an instructor wanted to supplement their course without adapting the entire text. I am not advocating this, as I think the text would be suitable as a whole for a course, but it is possible. This book is organized into Units, which are broken down into chapters. The unit and chapter organization makes sense for coverage of the material. In my introductory statistics course we do not cover linear regression, so I cover correlation earlier in my class. Since correlation is grouped into the Unit 3 (Additional Hypothesis Tests) it makes it a little more difficult to move out of this section and integrate elsewhere, but it is not a major concern for me. Interval: If you measure a variable at the interval level of measurement, it has the measurement properties of magnitude and equal intervals. You determine the most appropriate measure of dispersion as follows, depending on the nature of your data:

Psychology Statistics For Dummies Cheat Sheet (UK Edition)

Weighing up the advantages and disadvantages of each measure leads you to the following conclusion: the most appropriate measure of central tendency for a variable depends on the level of measurement of the variable and the nature of the distribution of scores within that variable. In general, the content was accurate. There were a few instances where the material was oddly worded or a confusing. For example, when covering hypothesis testing, an example claims that because temperature is allowed to vary 1 degree in either direction means that the standard deviation must be 1. This is not how standard deviation is defined and can be misleading to students. Later in the text, when interpreting a correlation of -1, the authors state “as X goes up by some amount, Y goes down by the same amount, consistently”. This is an inaccurate interpretation of correlation. X and Y are more than likely on different scales, so they would not change by the same amount. This is a very important distinction as correlation quantifies the relationship of standardized scores, while slope considers the scales of the variables. It was easy to single out one or two cases because the almost the entirety of the text is accurate. If I were going to write a statistics book, it would be very close to this. This is a readable textbook appropriate for an introductory statistics course in psychology. Examples given are succinct and easy to follow.

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The book is well-written (i.e., clear, concise, engaging). It is appropriate for an undergraduate taking their first statistics course.