# SPSS Statistics For Dummies

Published: 09-09-2020

The fun and friendly guide to mastering IBM’s Statistical Package for the Social Sciences

Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis.

Covering the latest release and updates to SPSS 27.0, and including more than 150 pages of basic statistical theory, it helps you understand the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and more. You’ll even dabble in programming as you expand SPSS functionality to suit your specific needs.

• Master the fundamental mechanics of SPSS
• Learn how to get data into and out of the program
• Graph and analyze your data more accurately and efficiently
• Program SPSS with Command Syntax

Get ready to start handling data like a pro—with step-by-step instruction and expert advice!

## Articles From SPSS Statistics For Dummies

11 results
11 results
SPSS Statistics For Dummies Cheat Sheet

Cheat Sheet / Updated 02-24-2022

IBM SPSS Statistics is an application that performs statistical analysis on data. To perform statistical analyses correctly, you need to know the level of measurement of the variables because it defines which summary statistics and graphs should be used. It also helps to know the most commonly used procedures in the Analyze menu and possible conclusions that you can reach after conducting a statistical test.

View Cheat Sheet
SPSS For Dummies Cheat Sheet

Cheat Sheet / Updated 02-14-2022

SPSS is an application that performs statistical analysis on data. Entering and manipulating information in the application can be done by using SPSS’s proprietary language, which is known as the Syntax command language, or more commonly, as Syntax. The language is quite like other programming languages, and it allows you to define variables (or use predefined ones), and to use them within statements, or to evaluate them with relational or logical operators. Good programmers always know to make their code accessible through the use of comments. Syntax can also be used in conjunction with Basic and Python.

View Cheat Sheet
How to Run an Analysis in SPSS Statistics

Article / Updated 08-15-2020

View Article
Modules You Can Add to SPSS

Article / Updated 08-15-2020

View Article
10 SPSS Statistics Gotchas

Article / Updated 08-15-2020

View Article
4 SPSS Statistics Licensing Options

Article / Updated 08-15-2020

View Article
How to Start SPSS Statistics

Article / Updated 08-15-2020

View Article
Interpreting Statistical Significance in SPSS Statistics

Article / Updated 08-15-2020

When conducting a statistical test, too often people jump to the conclusion that a finding “is statistically significant” or “is not statistically significant.” Although that is literally true, it doesn't imply that only two conclusions can be drawn about a finding. What if in the real world no relationship exists between the variables, but the test found that there was a significant relationship? In this case, you would be making a false positive error because you falsely concluded a positive result (you thought it does occur when in fact it does not). On the other hand, what if in the real world a relationship does exist between the variables, but the test found that there was no significant relationship? In this case, you would be making a false negative error, because you falsely concluded a negative result (you thought it does not occur when in fact it does). In the Real World Statistical Test Results Not Significant (p > 0.5) Significant (p < 0.5) The two groups are not different The null hypothesis appears true, so you conclude the groups are not significantly different. False positive. The two groups are different False negative. The null hypothesis appears false, so you conclude that the groups are significantly different.

View Article
SPSS Statistics Commonly Used Analyze Menus

Article / Updated 08-15-2020

The following table provides a list of some of the most commonly used procedures in the Analyze menu in SPSS Statistics. Menu Submenu Useful For Code Book Reports Provides a quick look at all your variables at once. The level of measurement automatically controls which summary statistics are displayed. Frequencies Descriptives Tells you how many of each category value you have. Most useful for categorical variables because you can run all of them at once. Descriptives Descriptives Gets basic scale variable information, such as the mean and standard deviation. Explore Descriptives Based on a famous book, Exploratory Data Analysis, looks at all kinds of variables as well as pairs of variables. Crosstabs Descriptives Tests to see if categorical variables are independent of each other or related to each other. Means Compare Means Calculates subgroup means and related statistics for dependent variables within categories of one or more independent variables. One-Sample T-Test Compare Means Tests whether the mean of a single variable differs from a specified value (for example, a group using a new learning method compared to the school average). Independent Samples T-Test Compare Means Tests whether the means for two groups differ on a continuous dependent variable (for example, females versus males on income). Paired Samples T-Test Compare Means Tests whether a significant difference exists in the mean under two conditions (for example, before versus after, or standing versus sitting). One-Way ANOVA Compare Means Tests whether the means for two or more groups differ on a continuous dependent variable (for example, drug1 versus drug2 versus drug3 on depression). Bivariate Correlation Correlate Determines the similarity in the way two continuous variables change in value from one case (row) to another through the data. Linear Regression Regression Predicts a continuous dependent variable from one or more continuous independent variables One Sample Nonparametric Tests Compares the distribution of a categorical dependent variable to population norms. Independent Samples Nonparametric Tests Tests whether the means or medians for two or more different groups differ on a dependent variable. Related Samples Nonparametric Tests Tests whether the means or medians of the same group differ under two conditions or time points. Univariate General Linear Model An extension of one-way ANOVA in which there is more than one independent variable. Multivariate General Linear Model An extension of one-way ANOVA in which there is more than one dependent variable. Repeated Measures General Linear Model An extension of the paired-samples t-test in which the same group is assessed under two or more conditions or time points. Binary Logistic Regression Used in situations similar to linear regression but the dependent variable is dichotomous. Multinomial Logistic Regression An extension of binary logistic regression in which the dependent variable is not restricted to two categories. Discriminant Classify Builds a predictive model for group membership based on the linear combinations of predictors that best separate the groups.

View Article
SPSS Statistics Chart to Show Relationships between a Pair of Variables

Article / Updated 08-15-2020

When choosing a graph, you need to know the level of measurement of the variables. The following table shows some of the graphs that can be used to display relationships between different types of variables. Categorical Dependent Scale Dependent Categorical Independent Clustered bar or paneled pie Error bar or boxplot Scale Independent Error bar or boxplot Scatter plot

View Article