If you’ve been looking for a self extracting GPower program online SPSS can provide the tools you need. It can use a number of different analyses to help you to work out the sensitivity of a range of binary hypothesis tests. Whenever you’ve got a null hypothesis and an alternative hypothesis and you want to know how accurate the tests you’ve used are, it’s this aspect of SPSS you need.

## Understanding Sampling Error

When using univariate analysis of variance SPSS extracting sample error is rather straightforward once you understand how to do it. Once you know the confidence intervals of your population and you’ve calculated the margin of error, you’ll already have a feel for the usefulness of your data.

Sampling error has to do with the deviation of a selected sample from the actual qualities and characteristics of the entire population. When you take a sample from any given population, there’s always going to be a chance that your selected participants differ in terms of their individual characteristics from those of the general population at large.

When you conduct research, you need to make sure that you limit bias as much as possible and that you choose a sample that is as representative of the entire population as it can be. Randomly selecting your participants using specially tested methods will allow you to be as unbiased as possible, but there’s still some level of chance that your sample won’t be quite as representative as you had hoped.

## The Importance of Power

The statistical power of any test you run is defined most simply as the probability that it’ll __reject a false null hypothesis__. As such, said power is inversely related to the chance of making a type II error. In SPSS extracting sample error and working out whether you’re going to end up making a type I or type II error is easy when you know how. The risk of making either of these errors during data management and analysis is determined by the level of significance, as talked about above, plus the __power of the test__ used.

Usually you’d be willing to accept a 5% chance of getting it wrong when you reject your null hypothesis. Trying to get this percentage down any lower in order to further minimize the risk of making a type I error would reduce your chances of detecting a real difference if it exists.

The best way to increase the power of your tests enough to avoid a type II error is to increase your sample size. Obviously, for practical reasons, this isn’t always possible.

## Using G Power for One-Way Within-Subjects ANOVA

Of the various ways you can use G Power repeated measures ANOVA, also known as one-way within-subjects analysis of variance, is among the simplest. In terms of statistics and using G Power repeated measures ANOVA is best described and thus understood by means of its alternative name as given here.

As with any ANOVA design, there are three particular specifics that must be defined in order for us to accurately describe the test. Consider these three aspects when you talk to an expert about the analysis of your data and you’ll understand exactly what they’re talking about.

- Think about how many factors are part of the design. Also known as independent variables, there can be several of these within a given ANOVA design. Within a one-way analysis, there is one factor.
- The next part to consider is how many levels a factor had. Much like the conditions generated by manipulation of a single independent variable, here referred to as a factor, you can have a number of levels. In clinical drug trials, for example, you might use drug dosages (your factor) at different concentrations (levels).
- Finally, you need to work out whether your factor operates within or between subjects. For a repeated measures ANOVA like the one we’re discussing in this article, your factor varies within a participant. To take our clinical trial example one step further, we can say that a participant who was given a range of drug dosages has experienced several levels of a factor. Now you can see why it’s also referred to as a repeated measures design.

## How to Cite G Power in APA

As always, you’re going to have to cite anything you do in the APA format. With a few tweaks here and there, it’s quite possible to convert SPSS and G Power styles to fit APA standards. Follow the steps below and you’ll learn how to cite G Power in APA format in no time at all.

- Each table you produce needs to be placed on a separate page after the list of references. This should be right at the end of your manuscript.
- The publisher of your research paper may have already set some guidelines with regard to font size and type, but if not, then the APA standard is Times New Roman at size 12.
- The size of your table dictates how large the margins should be, but as a rule, you can expect to have to make them at least 1 inch (2.54 cm).
- The one place where the APA rules allow you a bit of leeway is when you’re setting out your line spacing. You can choose between single spacing and one-and-a-half spacing.
- If you’ve resorted to the use of any abbreviations within your table, you’ll need to elaborate upon what they stand for by using a table note at the bottom of the relevant table. The same is true if there’s any other information there that’s not immediately understood by any reader.

If you need the all the functions available in a self extracting GPower program SPSS is the software package to use. Consider asking an expert statisticians for timely assistance with your research data analysis. Learning by example from the professionals is the best way to gain new skills. If you need a self extracting GPower program SPSS is just the package for you.