Dissertation Consultants

Statistical and Qualitative Analysis Techniques

The choice of statistical or qualitative analysis techniques in a doctoral dissertation depends on the research questions and research design, the nature of the data, and the type of data collected. Here is a brief overview of common techniques used in both types of analyses:

Statistical Analysis Techniques

  • Descriptive Statistics

Purpose: Summarizes and describes the main features of a dataset.
Techniques: Mean, median, mode, range, standard deviation.

  • Inferential Statistics

Purpose: Draws inferences about a population based on a sample of data.
Techniques: t-tests, analysis of variance (ANOVA), regression analysis, chi-square tests.

  • Regression Analysis

Purpose: Examines the relationship between one dependent variable and one or more independent variables.
Techniques: Linear regression, logistic regression, multiple regression.

  • Factor Analysis

Purpose: Identifies underlying factors that explain patterns of relationships within a set of observed variables.
Techniques: Exploratory factor analysis (EFA), confirmatory factor analysis (CFA).

  • Cluster Analysis

Purpose: Identifies groups of similar cases within a dataset.
Techniques: K-means clustering, hierarchical clustering.

  • Multivariate Analysis of Variance (MANOVA)

Purpose: Extends analysis of variance (ANOVA) to multiple dependent variables.
echniques: Assessing differences in means across multiple groups.

Qualitative Analysis Techniques

  • Thematic Analysis

Purpose: Identifies, analyzes, and reports patterns (themes) within the data.
Techniques: Coding, categorizing, and interpreting themes.

  • Grounded Theory

Purpose: Develops theories from the data, allowing patterns and concepts to emerge.
Techniques: Constant comparative analysis, theoretical sampling.

  • Content Analysis

Purpose: Analyzes the content of textual, visual, or audio data to identify patterns.
Techniques: Coding, categorizing, and quantifying content.

  • Case Study Analysis

Purpose: In-depth exploration of a single case or a few cases.
Techniques: Cross-case analysis, pattern matching.

  • Narrative Analysis

Purpose: Analyzes the stories people tell to understand the meaning-making process.
Techniques: Identifying narrative elements, thematic analysis.

  • Phenomenological Analysis

Purpose: Explores and describes the essence of lived experiences.
Techniques: Bracketing, identifying themes, interpreting meanings.

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