Module 4: Quantitative Data Analysis

Which of the following best describes quantitative analysis?

• It focuses on interpreting meanings from non-numerical data.
• It involves the systematic empirical investigation of social phenomena via statistical, mathematical, or computational techniques.
• It uses in-depth interviews and observations.
• It aims to generate new theories rather than test existing ones.

Answer explanation

Quantitative analysis is characterized by its reliance on numerical data and statistical methods to analyze and understand social phenomena.

What is a primary goal of quantitative analysis?

• To explore individual experiences in detail.
• To describe, explain, or predict phenomena through numerical data.
• To understand the subjective meanings people attach to their experiences.
• To conduct case studies for holistic understanding.

Answer explanation

Quantitative analysis seeks to identify patterns, relationships, and trends in data, often with the goal of generalizing findings to larger populations and explaining or predicting outcomes.

Which type of data is typically used in quantitative analysis?

• Textual data from interviews
• Audio recordings from focus groups
• Numerical data such as survey responses, statistics, and measurements
• Observational field notes

Answer explanation

Quantitative analysis deals with numerical data, which can be collected through various methods like surveys, experiments, and existing statistical records.

What is the first general procedure in quantitative analysis of data?

• Thematic analysis
• Interpretation of results
• Preliminary analysis of data
• Summarizing sample characteristics

Answer explanation

The first step in quantitative data analysis is preliminary analysis, which involves checking the raw data for errors, inconsistencies, and preparing it for further processing.

What does the "preliminary analysis of data" primarily involve?

• Testing hypotheses using inferential statistics.
• Editing, coding, and classifying raw data to eliminate errors and ensure consistency.
• Interpreting the findings in relation to existing theories.
• Constructing complex graphs and charts.

Answer explanation

Preliminary analysis of data is crucial for data cleaning and organization, ensuring the data is accurate and ready for statistical analysis.

The stage of quantitative analysis that highlights the main structural features of the study population (e.g., age, sex, occupation) is known as:

• Thematic analysis
• Interpretation of result and inferences
• Summarising sample characteristics
• Preliminary analysis of data

Answer explanation

Summarizing sample characteristics involves describing background variables of the study population using descriptive statistics like simple frequency distributions.

What is the "crux" of the data analysis procedure, where the researcher attempts to find answers to research questions and test hypotheses?

• Preliminary analysis of data
• Summarising sample characteristics
• Thematic analysis
• Interpretation of result and inferences

Answer explanation

Thematic analysis in quantitative research, as described by Obikeze (1990), is the core stage where researchers answer research questions and test hypotheses.

According to Obikeze (1990), which of the following is NOT one of the seven procedures for ordering issues for data analysis and presentation?

• Making a list of all relevant research issues.
• Ignoring peculiar features of the data.
• Constructing frequency distribution for each topic.
• Testing any explicit hypothesis.

Answer explanation

One of the procedures is identifying trends, variations, relationships or any peculiar features of the data, so ignoring them would be incorrect.

After data have been analyzed using descriptive statistics, what is the next step where generalizations and inferences are made?

• Preliminary analysis of data
• Thematic analysis
• Construction of frequency distribution tables
• Interpretation of result and inferences

Answer explanation

Interpretation of results and drawing inferences is the final stage where meaning is derived from the analyzed data, allowing for generalizations and conclusions.

What is the purpose of constructing frequency distribution tables?

• To conduct inferential statistical tests.
• To present raw data in an organized and understandable manner, showing how often each value or range of values appears.
• To establish cause-and-effect relationships.
• To generalize findings to a larger population without further analysis.

Answer explanation

Frequency distribution tables are a fundamental tool for organizing and summarizing data, making it easier to see patterns and distributions.