Your Qualitative Data Analysis Plan

Qualitative Study mostly answers the "Whys" and "Hows" of a particular problem or phenomenon. Case studies, narrative inquiry, phenomenology, ethnography, and Delpi usually constitute a decent qualitative Research. Data Analysis in the case of Qualitative Analysis is performed through Content or Thematic Analysis. However, a basic requirement is to follow and fulfill the pre-set standards and protocols of the step-by-step procedure from coding to analyzing data. The objective of using software tools like NVivo, MAXQDA, and ATLAS.Ti, and Dedoose for Qualitative Data Analysis is the extraction of practical themes and findings from the study.

However, dissertation projects vary with respect to the requirements, a common practice in the qualitative method is a phenomenological Research approach based on interpretative or transcendental phenomenological analysis. The high acceptability of the approaches is due to the least sample size they can process, that is 6 to 12, for the completion of the study. Grounded Theory Study is another known qualitative approach for encouraging a thematic analysis, where the structure is open, axial, or selective as the coding process bases on a relevant theory.

You may also require to justify and validate your results in the assigned qualitative data analysis. The reason why your discussion should consist of the possible biases which may have impacted your evaluation of the collected data. It must ideally include credibility, dependability, transferability, and confirmability to confirm the trustworthiness of your analysis and data.

Your Qualitative Data Analysis Plan

Finding a statistically significant association between or among quantifiable variables is the most commonly associated goal with Qualitative Studies. Analysis of Variance also called as ANOVA, Logistic Regression, Linear Regression and Structural Equation Modelling (SEM) are the most practiced in the domain. Descriptive Analysis is the first step to diving deeper into the Research question with the help of qualitative data leading to an inferential analysis aimed at producing statistically valid results. Most popular software tools like Stata, SAS, R, and SPSS are used to conduct qualitative studies using primary or secondary data with the thought of either finding a correlation between variables or presenting regression or correlation estimates.

Tables and plots are materialized for the presentation of data in the light of your study objectives in the description part of your quantitative data analysis. These tables from Descriptive Statistics present the basic variables of interest in form of frequencies, means, and medians. Moreover, continuous data is presented through Scatterplots, whereas binary or categorical data is on barplots. Another critical requirement for you is the demonstration of data cleaning or manipulation, and the organization of data in an adequate statistical model as an integral part of the Research Methodology under the head of your Descriptive Analysis.

Your quantitative data analysis's inferential part uses mathematical models to verify the results statistical significance. Many alternative quantitative inferential techniques can be used to provide the pertinent Research findings depending on the details of your investigation, with each statistical model being based on either a correlational or regression methodology. Learn more about our Biostatistics consulting services if you are doing a study in the biostatistics or health sciences that might call for a more thorough data analysis approach.

Research Methodology

Whatever your requirements are for data analysis, The Assignment Master helps students with every step of the procedure. We can assist you in creating and putting into action a personalized data analysis strategy that works for you. We have access to all the software, including NVivo, MAXQDA, and ATLAS.Ti, Dedoose, SPSS, Stata, R, SAS, and more, which is even better!