In STAGE NINE: Data analysis, we discuss the data you will have collected during STAGE EIGHT: Data collection. However, before you collect your data, having followed the **research strategy** you set out in this **STAGE SIX**, it is useful to think about the **data analysis techniques** you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on **(a)** the research questions/hypotheses you have set, **(b)** the research design you are using, and **(c)** the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses, as well as knowing the goal of your research design from STEP TWO: Research design in this **STAGE SIX: Setting your research strategy**. These two pieces of information - your research questions/hypotheses and research design - will let you know, **in principle**, the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words **in principle** and **may** because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the **nature of your data**. As you should have identified in STEP THREE: Research methods, and in the article, Types of variables, in the **Fundamentals** part of Lærd Dissertation, **(a)** not all data is the same, and **(b)** not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is **normal**, nor is the data when comparing groups necessarily **equal**, terms we explain in the Data Analysis section in the **Fundamentals** part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a **dependent t-test**), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection), the data may fail certain **assumptions** that are important to such a statistical test (i.e., **normality** and **homogeneity of variance**). As a result, you have to run another statistical test (e.g., a **Wilcoxon signed-rank test** instead of a **dependent t-test**).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the **data analysis techniques** you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A

Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation processThis is not always the case, but if you have had to write a

**Dissertation Proposal**or**Ethics Proposal**, there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the**Research Strategy**chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.REASON B

It takes time to get your head around data analysisWhen you come to analyse your data in STAGE NINE: Data analysis, you will need to think about

**(a)**selecting the correct statistical tests to perform on your data,**(b)**running these tests on your data using a statistics package such as SPSS, and**(c)**learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the**Fundamentals**part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e.,**STAGE SIX: Research strategy**), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection) is a sensible approach.

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of **STAGE SIX: Research strategy** is to have a **clear** research strategy that you can **implement** (i.e., **operationalize**). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point.