Data analyses

Data analyses

The data gathered for the qualitative research study is in the form of journal articles, trade journal reports and reviews of the marketing campaigns.

Descriptive research involves collecting data in order to test hypotheses or to answer questions about the opinions of people about some topic or issue. This kind of research is also called survey research. Research is used in most of the fields, including education, political science, sociology, and economics. A high percentage of all research studies are descriptive in nature. Descriptive data typically are collected through a questionnaire survey, interview or observation. Descriptive research is not as simple as it appears. It is, guided by the basic steps of the scientific and disciplined inquiry approach. The classification of the descriptive research depends on how the data are collected, through self-report of observation. The most common self-report approaches are questionnaires, telephone surveys, and interviews. This kind of research requires the collection of standardized, quantifiable information from members of a population or sample.

Data collection plays an important role in data analysis because poor data is not fixed through better analysis. This job is performed by trained interviewers or generally a research department which is specialized in data collection methods. There are different errors that take place in data collection process such as no sampling error. This error is caused by the factors other than sampling errors. The error is usually resulted in selected irrelevant and relatively poor elements of the population which may not be very effective in responding data collection forms. There are other factors as well such as: wrongs subjects for interview, wrong information and dishonest interviewers who cheat on data collection and fill fictitious forms. Sometimes there is a huge possibility in making non sampling errors. Even honest interviewers make errors while transferring wrong information in their survey forms and misrepresentation of the data often result in errors.

Data analysis is the crucial part of any research. Researches today use different softwares to analyze and present data. For example, Excel is used for data analysis and presentation. Similarly, there are many other softwares which now play a key role in statistical procedures in research. Data analysis is the process of data entry into computer files, estimating errors in data, tabulation and conducting various other statistical tests.

Data analysis in mixed methods research consists of analyzing the qualitative data using qualitative methods and the quantitative data using quantitative methods; therefore, knowing the steps in both forms of analysis is necessary in mixed methods research. For both forms of analysis, researchers go through the following similar set of steps: preparing the data for analysis, exploring the data, analyzing the data, representing the analysis, and validating the data. These procedures in quantitative research and qualitative research are different from each other.

Main data analyses

In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.

In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested.

It is important to obtain some indication about how generalizable the results are. While this is hard to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing this:

Cross-validation: By splitting the data in multiple parts we can check if analyzes based on one part of the data generalize to another part of the data as well.

Sensitivity analysis: A procedure to study the behavior of a system or model when global parameters are (systematically) varied. One way to do this is with bootstrapping.

As I mentioned before, data collection play an important role in data analyses. Data analyses are crucial part of any research. Because we can use several type of sortwares, which help to analyze the data. The most common self-report approaches are questionnaires, telephone surveys, and interviews. This kind of research requires the collection of standardized, quantifiable information from members of a population or sample.