Welcome to week five of this course on research methods. This week, we will focus on the final step of the research cycle, which is data analysis. In the previous sessions, we were able to differentiate the various methods of qualitative and quantitative research, as well as the different methods of data collection. In this session, we will learn the various ways of data analysis ranging from data preparation and aggregation, descriptive statistics, and inferential statistics. By the end of the course, you will be able to identify the type of analysis to perform on your data in order to answer your research questions. Firstly, let us understand the term analysis. To analyze means computing certain measures along with searching for patterns of relationship that exists among groups of data. The analysis in any research projects involves summarizing the mass of data that has been collected and presenting the results in a way that communicates the most important findings or features. Before we get in-depth into quantitative analysis, let me briefly explain the difference between qualitative and quantitative analysis. Firstly, both qualitative and quantitative analysis involve labeling and coding all of the data, so similarities and differences can be recognized. The analysis of qualitative research aims to uncover and understand the big picture. Responses from even and unstructured and qualitative interview can be entered into a computer, to be coded and analyzed. The qualitative research however, has no system of precoding. Therefore, a method of identifying and labeling or coding data needs to be developed and that is called content analysis. Content analysis can be used when qualitative data has been collected through interviews, focus groups, observations, and document analysis. It is a procedure for the categorization of verbal or behavioral data, for purposes of classification, summarization, and tabulation. Therefore, content analysis involves coding and classifying data, also referred to as categorizing and indexing, and the aim of content analysis is to mix, make sense of data collected, and to highlight the important messages, features, or findings. In the following videos, we shall get more in-depth information on qualitative analysis. Now the analysis of quantitative research involves; analyzing frequencies of variables, differences between variables, and statistical tests designed to estimate the significance of the results and the probability that they did not occur by chance. Analysis of quantitative data can be done in two ways, as descriptive analysis and as inferential analysis. We shall discuss these in more detail in the following videos. Before analyzing quantitative data, it is important to identify the measurement scale of the data type. Do you remember the various methods of measurement you learned in the previous session? To briefly remind you, there are four basic measurement scales: Nominal, ordinal, interval, and ratio. You may refer to the introduction video in week four for more information. In quantitative analysis, the data structure plays a key role. What exactly do I mean by data structure? This refers to the way in which the data can be visualized and categorized in different ways. Largely, as a result of the method of data collection. Thinking about the data structure will force you, as the researcher, to focus on what constitutes data summarization. It will also help you to identify the factors that cannot be coded. Remember, that qualitative data analysis involves identification, examination, and interpretation of patterns and themes in textual data. And determines how these patterns and themes helped answer the research questions at hand. In quantitative data analysis, information is represented numerically and analyzed through statistical approaches, to draw conclusions about relationships between different variables. That is all about this introduction video on qualitative and quantitative analysis. In the next videos, we shall go deeper into the various ways of qualitative and quantitative analysis.