Stats & SPSS resources. Data analysis. SPSS Data files and exercises. Data files Throughout the SPSS Survival Manual you will see examples of research that is taken from a number of different data files, survey.zip, error.zip, experim.zip, depress.zip, sleep.zip and staffsurvey.zip.

To use these files, which are available here, you will need to download them to your hard drive or memory stick. Once downloaded you'll need to unzip the files. To do this, right click on the downloaded zip file and select 'extract all' from the menu. You can then open them within SPSS. (To do this, start SPSS, click on the Open an existing data source button from the opening screen and then on More Files. Survey.sav This is a real data file, condensed from a study that was conducted by my Graduate Diploma in Educational Psychology students.

Download survey.zip Download PDF of questionnaire and codebook used for survey.zip (Adobe Reader required) Download PDF of full questionnaire for survey.zip (Adobe Reader required) Download the complete syntax file surveysyntax.sps. Menu. OpenEpi provides statistics for counts and measurements in descriptive and analytic studies, stratified analysis with exact confidence limits, matched pair and person-time analysis, sample size and power calculations, random numbers, sensitivity, specificity and other evaluation statistics, R x C tables, chi-square for dose-response, and links to other useful sites.

OpenEpi is free and open source software for epidemiologic statistics. It can be run from a web server or downloaded and run without a web connection. A server is not required. The programs are written in JavaScript and HTML, and should be compatible with recent Linux, Mac, and PC browsers, regardless of operating system. (If you are seeing this, your browser settings are allowing JavaScript.) Test results are provided for each module so that you can judge reliability, although it is always a good idea to check important results with software from more than one source. Gapminder: Unveiling the beauty of statistics for a fact based world view. Businessinsider. Online Statistics Education: A Free Resource for Introductory Statistics. Developed by Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University OnlineStatBook Project Home This work is in the public domain.

Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study ( Project Leader: David M. If you are an instructor using these materials in a class we would appreciate hearing from you. Version in PDFe-Pub (e-book)Interactive e-book (for IOS and OS X) Table of Contents Mobile This version uses formatting that works better for mobile devices. Rice Virtual Lab in Statistics. What is the difference between categorical, ordinal and interval variables? What is the difference between categorical, ordinal and interval variables?

In talking about variables, sometimes you hear variables being described as categorical (or sometimes nominal), or ordinal, or interval. Below we will define these terms and explain why they are important. Categorical A categorical variable (sometimes called a nominal variable) is one that has two or more categories, but there is no intrinsic ordering to the categories.

Difference Between Linear and Logistic Regression: Linear Regression vs Logistic Regression. Linear vs Logistic Regression In statistical analysis, it is important to identify the relations between variables concerned to the study.

Sometimes it may be the sole purpose of the analysis itself. One strong tool employed to establish the existence of relationship and identify the relation is regression analysis. The simplest form of regression analysis is the linear regression, where the relation between the variables is a linear relationship. In statistical terms, it brings out the relationship between the explanatory variable and the response variable.

Mann-Whitney U Test in SPSS Statistics. Introduction The Mann-Whitney U test is used to compare differences between two independent groups when the dependent variable is either ordinal or continuous, but not normally distributed.

For example, you could use the Mann-Whitney U test to understand whether attitudes towards pay discrimination, where attitudes are measured on an ordinal scale, differ based on gender (i.e., your dependent variable would be "attitudes towards pay discrimination" and your independent variable would be "gender", which has two groups: "male" and "female").

Alternately, you could use the Mann-Whitney U test to understand whether salaries, measured on a continuous scale, differed based on educational level (i.e., your dependent variable would be "salary" and your independent variable would be "educational level", which has two groups: "high school" and "university"). The Mann-Whitney U test is often considered the nonparametric alternative to the independent t-test although this is not always the case.

The Distribution of Independent Variables in Regression Models. I often hear concern about the non-normal distributions of independent variables in regression models, and I am here to ease your mind.

There are NO assumptions in any linear model about the distribution of the independent variables. A comparison of the Pearson and Spearman correlation methods - Minitab Express. The Pearson and Spearman correlation coefficients can range in value from −1 to +1.

For the Pearson correlation coefficient to be +1, when one variable increases then the other variable increases by a consistent amount. This relationship forms a perfect line. The Spearman correlation coefficient is also +1 in this case. Pearson = +1, Spearman = +1 If the relationship is that one variable increases when the other increases, but the amount is not consistent, the Pearson correlation coefficient is positive but less than +1.

Cohen's d.