All About ANOVA & How SPSS Help Conduct It

All About ANOVA & How SPSS Help Conduct It


You must have seen students getting SPSS help for various kinds of data analysis techniques. Researchers and PhD scholars deal with numerous data sets, large or small, simple or complex, and they analyse those data sets using different techniques like ANOVA, MANOVA, regression, etc. To conduct these tests efficiently they use different tools like SPSS, STATA, NVIVO, R, and many more. In this blog post, we will discuss the ANOVA test, its types, examples, and its uses. If you wish to empower your knowledge about ANOVA and want to understand how help with SPSS can make you learn it in a better way, then read this blog further.

What do you understand by ANOVA?

ANOVA stands for Analysis of Variance. It is a type of statistical method which is used to find any significant differences between the mean values of two or more groups of data sets. In this data analysis test, the null hypothesis signifies that all the groups of data sets have the same mean value and any difference found is just a random chance. But what exactly does ANOVA do with that mean difference? It actually compares the mean difference of one independent and one dependent variable by examining means from three or more groups of data sets. ANOVA helps you differentiate between different groups and how they are different from each other. It checks if two distinct groups are statistically equal or similar. Since this data analysis test is a bit complex procedure, hence it is frequently used by academic researchers.

How does ANOVA work?

As we have already mentioned above, the ANOVA test is a type of statistical analysis method which calculates the difference between the means of two or more data groups. To know the significant difference between the two groups, you can run a t-test, but what if you have more than two groups? In such a case, you will use the ANOVA test. It will allow you to compare multiple groups altogether and find the significant difference between the groups. It is like you are running multiple t-tests at the same time and it is a very time-saving method. That way you will be able to avoid the errors which were likely to happen when comparing only two groups at a time. Though the ANOVA test and the T-test work behind the same logic, ANOVA compares the changes within each sample against the changes between each sample.
For example, let’s say you want to compare the heights of three people and want to know the significant difference in their heights for which you measure their heights in three different situations: before breakfast, after breakfast, and after lunch.
You will calculate the mean height for each individual for each of the above-mentioned time periods. You will use these values in the formula of the ANOVA test. As a final outcome, you will get a significant difference in their heights for those three time periods.

Types of ANOVA tests

There are mainly two types of ANOVA tests: One Way ANOVA and Two way ANOVA.

    1. One-way or unidirectional ANOVA test

      One-way ANOVA is used to compare three or more independent variable groups and to check if these groups are significantly similar or different. In this experiment, the researcher has to observe the changes in a response variable according to the changes in the levels of one single factor. For example, if a farmer wants to know about the average yield of a particular crop when three different categories of fertilisers are used, then he /she can find it by comparing the mean values of three different types of fertilisers (one single factor) and the crop’s yield (response variable).

    2. Two-way ANOVA test

      A two-way ANOVA test is used to find the effect of two factors on a single dependent variable, that is how a response variable is affected by the two factors. As we mentioned an example above: if the farmer wants to know how the crop yield after using three different fertilisers along with in what type of weather conditions the crop was planted then it will be considered a two-way ANOVA experiment because the farmer is taking two factors in consideration: one is the type of fertiliser and second is the type of weather.

When should you use ANOVA?

    1. You should use ANOVA when you want to compare two or more groups against each other. For example, if you have the average height of four different types of trees in your garden and you want to know if they are significantly different from each other, then rather than comparing individual trees with the rest of the three trees, run the ANOVA test and compare all of them altogether. You will get the desired result.
    2. You can use ANOVA when you want to test how well different groups fit together. For example, if you want to know from a group of trees of the same kind, whether they are of the same size or bigger than each other.
    3. As mentioned earlier, it is a statistical analysis method, which is used to identify any significant difference between the mean values of two groups of data sets.

How to use ANOVA

We will explain this through an example. Suppose you want to compare the heights of all your male and female classmates. Here’s how you will do that with ANOVA:

    1. Collect the data (height of every classmate).
    2. Calculate the average height of all the classmates.
    3. Calculate the average height of two groups individually (boys and girls).
    4. Find the difference between individual means and overall means and square them.
    5. Add all the squared differences. This is called as “sum of squares total (SST)”.
    6. Divide the squared difference by the number of people in each group. This will tell how much each group contribute to the overall SST.

Limitations of ANOVA

    1. ANOVA is not flexible enough in regard to the number of groups. Instead of ANOVA, you can use other more powerful tests.
    2. It has a limitation that, it can be used only when the dependent variable is continuous. If you have a categorical dependent variable then you should use the Chi-square test instead.
    3. ANOVA cannot work on multiple dependent variables simultaneously.

SPSS data analysis


ANOVA is a powerful data analysis technique and you can take the help of the SPSS data analysis program from experts if you still face problems in conducting this test. We have tried to provide every crucial information about ANOVA and you must have noticed that. The blog carried information regarding what is ANOVA, how to conduct it, in what situations it should be used, and what are its limitations. If you still face issues with your data analysis techniques, even if other than ANOVA then you should get professional help from some credible sources. Good luck with your PhD and data analysis tests!

Leave a Reply