Quantitative vs Qualitative Research - Key Concepts

Flashcards covering major concepts, terms, and procedures from the notes on Quantitative vs Qualitative Research, including design, sampling, analysis, interpretation, and ethics.

What is the primary aim of Quantitative research?

A systematic investigation that collects numerical data to test hypotheses, measure variables, and find patterns or relationships; it emphasizes objectivity and measurement.

What is the primary aim of Qualitative research?

An in-depth exploration of meanings, experiences, and social phenomena using words, images, or observations; it emphasizes understanding and interpretation.

Name the four quantitative study types listed under 'Types & Definition' for quantitative research.

Descriptive, Correlational, Experimental, Quasi-Experimental.

Name the qualitative research types listed under 'Types & Definition'.

Phenomenology, Grounded Theory, Ethnography, Case Study, Narrative.

Define Descriptive research.

Describes characteristics of a population (e.g., survey of favorite study methods).

Define Correlational research.

Examines relationships between variables (e.g., link between sleep hours and grades).

Define Experimental research.

Tests cause-effect relationships (e.g., testing a new teaching strategy).

Define Quasi-Experimental research.

Similar to experimental but lacks full control (e.g., comparing two classes without random assignment).

Define Phenomenology.

Studies lived experiences (e.g., experiences of first-time voters).

Define Grounded Theory.

Builds theory from data (e.g., developing a model of peer support).

Define Ethnography.

Studies culture or group behavior (e.g., observing a sports team).

Define Case Study.

In-depth investigation of one case (e.g., a school's reading program).

Define Narrative.

Collects and analyzes stories (e.g., life stories of working students).

What does the 'Background of the Study' cover in Quantitative vs Qualitative research?

Quantitative: problem, knowledge gap, significance using numerical data; Qualitative: context, social issue, and the need to explore experiences/meanings.

Hypothesis (Quantitative) vs Purpose (Qualitative).

Hypothesis: a testable prediction of the relationship between variables. Purpose: states the intention to explore and understand a phenomenon.

Provide an example of a Hypothesis (Quantitative).

H1: There is no significant relationship between gadget use and sleep quality.

What is the SOP in Quantitative research vs Research Questions (RQ) in Qualitative?

SOP uses measurable variables; usually asks about relationship/effect. RQ is open-ended; asks how or why to explore experiences.

What is the difference between Review of Related Literature (Quantitative) and Literature Review (Qualitative)?

Quantitative: focuses on summarizing past quantitative studies, statistical findings, and variables to identify gaps. Qualitative: focuses on synthesizing past studies to understand context, themes, and perspectives.

Theoretical Framework vs Conceptual Framework vs Theoretical Lens (Qualitative vs Quantitative).

Theoretical Framework: uses an existing theory to explain variables. Conceptual Framework: shows how variables relate in a diagram/model. Theoretical Lens: uses a perspective to guide interpretation of data.

What is a Theoretical Framework?

Uses an existing theory to explain variables.

What is a Conceptual Framework?

Shows how variables relate in a diagram/model.

What is a Theoretical Lens?

A perspective (e.g., feminist lens) used to guide interpretation of data.

What are Delimitations vs Limitations?

Delimitations: scope and boundaries (variables, population). Limitations: weaknesses beyond control (sample size, time).

How do Quantitative and Qualitative research differ in Research Design?

Quantitative: Structured and fixed (e.g., experimental, descriptive, survey). Qualitative: Flexible and emergent (e.g., case study, phenomenology).

Population & Sampling (Quantitative) vs Research Participants (Qualitative).

Quantitative uses probability sampling for generalization; Qualitative uses small, purposive samples for depth.

What is Probability Sampling and why is it used in Quantitative Research?

Every member of the population has a known chance of being selected; used to enable generalization.

Why is Non-Probability Sampling common in Qualitative Research?

Because it focuses on depth and relevance rather than generalization.

Describe Simple Random Sampling.

Everyone has an equal chance of being chosen.

Describe Stratified Random Sampling.

The population is divided into groups (strata), then random sampling is conducted within each strata.

Describe Convenience Sampling.

Selecting participants who are easiest to reach.

Describe Purposive Sampling.

Choosing participants who have specific knowledge or experience relevant to the study.

Describe Systematic Sampling.

Every kth member of the population is chosen.

Describe Cluster Sampling.

The population is divided into clusters; entire clusters are randomly chosen.

Describe Snowball Sampling.

Current participants refer new participants with the same characteristics.

Describe Quota Sampling.

Selecting participants based on a set quota or characteristic.

Research Locale

Describes the place where data is gathered and why it was chosen; qualitative emphasizes the meaning of the setting.

Research Instruments

Quantitative: structured tools like questionnaires, tests, and experiments. Qualitative: unstructured/semi-structured tools like interview guides, observation checklists.

Role of the Researcher in Quantitative vs Qualitative.

Quantitative: objective, remains detached to avoid bias. Qualitative: researcher is part of the process; reflexivity and building rapport are crucial.

Data Collection methods

Quantitative: surveys, experiments, structured observations. Qualitative: interviews, focus groups, field notes, participant observation.

Data Analysis (Quantitative vs Qualitative).

Quantitative: statistical computation (mean, median, mode, t-test, chi-square, correlation, regression). Qualitative: coding, thematic analysis, narrative analysis.

Test of Significant Relationship (Correlation/Chi-Square).

Purpose: To check if two variables are related. Tools: Pearson r, Spearman rho, Chi-Square.

How to interpret correlation results (p-value and r).

State hypotheses; compare p-value to alpha (usually 0.05). If p ≤ 0.05, reject H0. Interpret r: 0.00–0.19 very weak; 0.20–0.39 weak; 0.40–0.59 moderate; 0.60–0.79 strong; 0.80–1.00 very strong.

Test of Significant Difference (T-Test/ANOVA).

Purpose: check if two or more groups have different means. Tools: Independent t-test, Paired t-test, ANOVA.

How to interpret significant differences in t-test/ANOVA.

If p ≤ 0.05, reject H0 and note which groups differ (post-hoc tests for ANOVA). If p > 0.05, fail to reject H0.

Results & Discussion: Quantitative vs Qualitative.

Quantitative: presents findings with tables/graphs/stats and states whether hypotheses are accepted or rejected. Qualitative: presents themes, patterns, and direct quotes with interpretation.

Findings, Conclusions, Recommendations (Quantitative) vs Implications & Future Directions (Qualitative).

Quantitative: numerical findings, generalizations, recommendations, and future quantitative studies. Qualitative: themes, implications for theory/practice/policy, and possibilities for further qualitative or mixed-method research.

Example interpretation: p=0.03 and r=0.65.

There is a significant relationship between the variables, and the strength is strong.

Example interpretation: p=0.12 in a two-group comparison.

There is no significant difference between groups (fail to reject H0).