When you use SPSS hypothesis testing becomes much simpler. Path analysis SPSS allows you to collect and analyze data to answer your research questions. In this case, you have a set of independent and dependent variables and must work out whether any differences in the dependent variables can be explained by variation in the independent ones. SPSS can help you do this quickly and relatively painlessly. It’s just a matter of getting to grips with how the program actually works.

## Types of Hypotheses

It is important to understand the various definitions associated with a hypothesis. Oftentimes, you’ll find that the type of hypothesis you choose actually affects how you calculate the outcome of inferential statistical tests.

It’s best to start from zero, quite literally. Your null hypothesis (H0) states that it is possible for there to be no difference between conditions and no association between variables.

Compare this to your experimental hypothesis, also known as an alternative hypothesis (H1), which predicts a difference between conditions or an association between variables.

All conclusions are expressed with relation to your null hypothesis. That is to say, that you cannot directly accept your alternative hypothesis. Instead, you must express such a conclusion in the form of rejecting your null hypothesis in favor of the alternative.

Going a step further from simple alternative hypotheses, let’s consider one- and two-tailed hypotheses. Whereas a one-tailed hypothesis predicts that there will be a statistically significant effect observed and also speaks to the direction of this difference or association, a two-tailed hypothesis accepts that a significant effect may go in either direction.

## Formulating a Hypothesis

When you’re observing a process, you automatically make assumptions about a potential causative relationship between different variables. Aside from its uses in data management and hypothesis formulation IBM SPSS can be used to actually analyze your data and report conclusions based on the results of a number of complex statistical tests.

One of the simplest aspects of hypothesis formulation IBM SPSS lets you deal with has to do with the t-test. You can easily work with samples that are independent of one another or paired together. Chi square SPSS will help you work out which test you need to start working with your null and alternative hypotheses.

## Basic Interpretation of Hypothesis Testing in SPSS

It’s pretty simple to learn how to conduct interpretation of hypothesis testing in SPSS when you know enough about what a hypothesis actually is. All statistical analysis requires that you understand exactly what is being done to your data in order to achieve a result you can use to reach a useful conclusion.

Let’s consider one of the most fundamental aspects of hypothesis testing: the level of statistical significance as represented by the p-value. Without this, you cannot say whether your results could have occurred by pure chance and actually have no relation to your chosen variables.

The p-value is the probability of getting a result equal to or more extreme than what was observed when assuming a given hypothesis. The significance of your results grows larger as the p-value shrinks and, as long as your p-value is equal to or less than the pre-set threshold value that determines the level of significance, you won’t have to reject your hypothesis.

## A Step-by-Step Approach to Hypothesis Testing

There are a number of ways of approaching your null and alternative hypotheses. One such way is to conduct hypothesis testing using regression in SPSS. By using multiple regression, you can predict a variable based on the values of several other variables. However, be aware that you cannot imply causal relationships between variables in this way.

Ultimately, all hypothesis testing using regression in SPSS results in your being able to reach a conclusion based on your null hypothesis. Remember that you cannot make a statement based on the alternative hypothesis, and you should instead speak in terms of the null hypothesis. That is to say, you cannot either accept or reject the alternative (H1) without referring to the null (H0). In fact, even when you are technically accepting H1, you have to state this in terms of your rejection of H0.

It’s relatively simple to perform multiple regression in SPSS when you follow a set of instructions like those below, but to really understand what’s going on, you’ll have to speak to a professional statistics expert.

- Select the Analyze menu, choose the Regression option and then click Linear. This will cause the linear regression dialog box to pop up.
- Choose a dependent, or criterion, variable and move it to the associated box. Do the equivalent for the independent, or predictor, variables. Leave the method as Enter unless you already know how to use SPSS well.
- Click the Statistics button to access the associated dialog box. Check the Estimates box in the Regression Coefficients section.
- Check the Model Fit and Descriptives boxes. Leave the rest of the boxes unchecked if you’re sticking with the Enter method.
- Click the Continue button and return to the Linear Regression dialog box. Now you can click OK to see a table of descriptive statistics and the second table with correlations between each pair of variables. You’ll also receive a few more tables with a model summary, ANOVA results, plus a coefficients table containing B values, standard errors, Beta values, t values and p values. This is all very useful to see but the details of these values go beyond the scope of this article.

Make sure you get to grips with the statistical principles underlying all the different modes of SPSS hypothesis testing. You can contact a professional expert to help you learn everything you need to know to make the most of your raw data. Statistics is much simpler and thus more useful when you have a guiding hand on your shoulder.