# Psychology Statistics For Dummies

Published: 01-29-2013

The introduction to statistics that psychology students can't afford to be without

Understanding statistics is a requirement for obtaining and making the most of a degree in psychology, a fact of life that often takes first year psychology students by surprise. 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 you with the step-by-step instructions necessary for carrying out data analysis.

Psychology Statistics For Dummies:

• Serves as an easily accessible supplement to doorstop-sized psychology textbooks
• Provides psychology students with psychology-specific statistics instruction
• Includes clear explanations and instruction on performing statistical analysis
• Teaches students how to analyze their data with SPSS, the most widely used statistical packages among students

## Articles From Psychology Statistics For Dummies

5 results
5 results
Psychology Statistics For Dummies Cheat Sheet (UK Edition)

Cheat Sheet / Updated 06-26-2021

You may be more interested in studying psychology than in crunching numbers, but knowing psychology statistics is essential if you’re going to make all that research data stack up, and have other people understand it. This Cheat Sheet helps you out with some basic concepts in psychology statistics.

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Choosing the Right Measure of Dispersion in Psychology Statistics

Article / Updated 06-24-2021

The measures of dispersion you use in psychology statistics show you the spread or variability of the variable you are measuring. The three main ones are the range, the interquartile range, and the standard deviation. Getting to know the range, interquartile range, and standard deviation The three most important measures of dispersion are defined as follows: The range is the difference between the highest score and the lowest score in a variable. These are the values that have been scored by participants in the study, and not necessarily the highest and lowest possible scores. The interquartile range is the difference between the upper quartile and the lower quartile in a set of ordered scores. Quartiles are formed by dividing a set of ordered scores into four equal-sized groups. 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. Working out which measure of dispersion to use You determine the most appropriate measure of dispersion as follows, depending on the nature of your data: Data measured at the nominal level: Because all three measures of dispersion require data to be ranked or summed, none of them are appropriate for data measured at the nominal level. 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. Data measured at the interval/ratio level: All three measures of dispersion we have examined are appropriate. The standard deviation is usually preferable. However, the standard deviation (or variance) isn’t appropriate when there are extreme scores and/or skewness in your data set. In this situation the interquartile range is usually preferable.

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Choosing between Mode, Median and Mean in Psychology Statistics

Article / Updated 03-26-2016

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Determining the Role of Variables in Psychology Statistics

Article / Updated 03-26-2016

In psychology statistics, research studies which involve collecting quantitative data (any data that can be counted or rendered as numbers) usually require you to collect and store data on a data sheet about several variables. When you conduct your statistical analyses on this data, you need to know what role each variable played in your research design. Generally speaking, you classify variables in psychology statistics as independent variables, dependent variables or covariates. Independent variables Independent variables are sometimes referred to as predictor variables. Strictly speaking, an independent variable is a variable that you manipulate so that you can study how the changes in the independent variable influence changes in other variables. In some cases, you refer to variables as independent variables even when you’re not directly manipulating them.. This type of independent variable is a quasiindependent variable. Dependent variables Dependent variables are sometimes referred to as outcome variables or criterion variables. A dependent variable is usually the variable that you expect to change when you manipulate the independent variable. In other words, the dependent variable is the variable that the independent variable affects. Therefore, the dependent variable is so called because its value depends on the value of the independent variable (at least in theory). Covariates A covariate is a broad term used for a variable in a research design that’s neither an independent nor a dependent variable. In some designs you use a covariate to take account of other factors that might influence the relationship between the independent and dependent variable. A good research design measures these variables so that you can account for their influence. Within this research design, these variables are covariates. Covariates can also exist in research designs where no independent or dependent variables exist.

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Looking at Levels of Measurement in Psychology Statistics

Article / Updated 03-26-2016

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. The measurement properties that the values in a variable can possess are: Magnitude: This means that you can order the values in a variable from highest to lowest. Equal intervals: This means that a unit difference on the measurement scale is the same regardless of where that unit difference occurs on the scale. True absolute zero: The true absolute zero point means that the zero point on the measurement scale is the point where nothing of the variable exists and, therefore, no scores less than zero exist. These three measurement properties enable you to classify the level of measurement of a variable into one of four types Nominal: This means that a variable has none of the three measurement properties. You measure a variable at the nominal level when you’re using the numbers in the variable only as labels. Ordinal: If you measure a variable at the ordinal level then the values on the variable have the measurement property of magnitude only. You measure a variable at the ordinal level when the scores in the variable are ordered ranks. Interval: If you measure a variable at the interval level of measurement, it has the measurement properties of magnitude and equal intervals Ratio: if you measure a variable at the ratio level of measurement, it has the measurement properties of magnitude, equal intervals and a true absolute zero.

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