Quantitative vs Qualitative Research - Vocabulary Flashcards

Objectives

• End of lesson expectations: describe quantitative research; apply qualitative and quantitative descriptions to various objects; discuss the strengths and weaknesses of quantitative research.

Definition

• Research definition (from Western Sydney University, 2020):

  • The creation of new knowledge and/or use of existing knowledge in a new and creative way to generate new concepts, methodologies, and understandings.

  • May include synthesis and analysis of previous research to the extent that it leads to new and creative outcomes.

• Quantitative research characteristics (from discussion):

  • Uses numeric data and statistics for gathering and analyzing data.

  • Considered more rigorous, reliable, and precise.

  • Employs deductive reasoning (general to specific) to generate predictions that are tested in the real world.

Quantitative vs Qualitative Research

• Qualitative research:

  • Used to understand thoughts and experiences; gather in-depth insights on topics not well understood.

• Quantitative research:

  • Used to test or confirm theories/assumptions; establish generalizable facts about a topic.

Standards (Qualitative vs Quantitative)

• Qualitative

  • Mental survey of reality; results from social interactions

  • Researchers’ involvement with the object/subject

  • Expression of data, data analysis, and findings

  • Personal, subjective engagement; data in words, visuals, or objects

  • Occurs gradually; aims to preserve natural setting

  • Multiple methods; exists in the physical world

  • Revealed by descriptions of circumstances or conditions

  • Less observer-controlled; more subjective

• Quantitative

  • Cause-effect relationships; outcomes based on statistics

  • Objective data; least involvement by the researcher

  • Numerals, statistics; plans all research aspects before collecting data

  • Control or manipulation of research conditions by the researcher

  • Verbal language is minimal; data primarily numeric

  • Structured instruments; science-method approach

  • Can be more readily generalized due to controlled conditions

Purpose

• Qualitative:

  • Makes social intentions understandable; explore meanings behind actions

  • Style of expression: personal, less formal

  • Sampling: purposive or based on chosen samples

  • Data analysis: thematic coding, narrative/interpretive approaches

• Quantitative:

  • Evaluate objectives and examine cause-effect relationships

  • Style of expression: impersonal, scientific, or systematic

  • Data analysis: mathematically based methods

  • Sampling: random sampling is preferred; representative samples

Controllability and Generalizability

• Quantitative research should occur in environments where variables can be identified and controlled.

• Outcomes are based on large sample sizes that can be generalized to an entire population.

• Results rely on statistics that are observable and measurable using structured instruments.

Replicability

• Quantitative research should be replicable by other teams of researchers, leading to similar outcomes.

Strengths of Quantitative Research

1) Use of statistical methods for data analysis, enabling reliable, precise, and objective generalizations.
2) Large-scale research capability: random sampling allows representation of the whole; data collected quantitatively (surveys, tests) can speed up gathering; in-depth interviews are not always necessary.
3) Data can be presented in graphical or tabular form, facilitating quick interpretation.

Weaknesses of Quantitative Research

1) Large sample sizes require significant time, money, and effort.
2) Statistical analysis often requires an expert (statistician) for inferential statistics (e.g., T-test, Chi-square, ANOVA) or specialized descriptive statistics.
3) Quantifying observations can oversimplify phenomena, potentially omitting respondents’ thoughts and experiences; motivates mixed-methods use (quantitative + qualitative).

Appropriate Research Approach

• There is value in choosing the most appropriate approach for a given study, not sticking rigidly to qualitative or quantitative.

• Triangulation (using multiple approaches) can enhance research depending on the nature of the question.

Why Use Quantitative Research?

• Produces results with precise measurement and in-depth data analysis.

• Aims for objective understanding of people, objects, places, and events, minimizing researcher bias.

• Relies on reliable measurement instruments or statistical methods.

• Useful for identifying relationships between characteristics and reasons behind them; describes personality traits or group-level relationships.

Kinds of Quantitative Research

• Two main types:

  • Experimental research

  • Non-experimental research

Experimental vs Non-Experimental (Overview)

• Experimental research adheres to a scientific design with hypotheses, manipulated variables, measurable outcomes, and a controlled environment; tests hypotheses (hypothesis testing / deductive approach).

• Non-experimental research describes data and explores relationships without manipulating conditions; includes both qualitative and quantitative data.

Experimental Research

• True Experimental Research: randomized samples to identify cause-effect relationships between variables.

  • Example: Sunlight effect on plant growth with three setups:

  • Set A: ample sunlight

  • Set B: limited sunlight

  • Set C: no sunlight

  • All plants in the same soil, equal water; observe results after a period.

• Quasi-Experimental Research: lacks random assignment; uses assigned groups.

  • Example: Effect of height on milk brand preference; height-based group assignment instead of randomization.

  • True experiments require random assignment; quasi-experiments assign groups based on characteristics (e.g., height).

Non-Experimental Research

• Descriptive Research: describes factors/variables/phenomena in nature; uses descriptive statistics (mean, median, mode).

  • Example: Identify factors contributing to food spoilage via survey; temperature as a factor may emerge as most influential.

• Comparative Research: compares two variables to identify potential causation; involves two or more groups and one independent variable.

  • Example: Attendance at a summer program and class participation; compare groups who did vs. did not attend.

• Correlational Research: identifies relationships between two variables; does not imply causation.

  • Example: Relationship between sleep length and student productivity; data on sleep (bedtime/wake time) and productivity (activities completed daily); longer sleep tends to relate to higher productivity.

Legend and Study Designs (Pages 25–27)

• Legend:

  • X = Treatment or Intervention

  • 01 = Pretest

  • O = Observation

  • RS = Random Selection

  • O2 = Posttest

  • EG = Experimental Group

  • CG = Control Group

• True Experimental Research designs: 1) Pretest-Posttest Controlled Group Design

  • Diagram: EG 01 X 02 RS CG 01 02
    2) Posttest Only Controlled Group

  • Diagram: EG X 02 RS CG 02 02
    3) Solomon Four Group

  • Diagram: EGI 01 ------ X 02 CG1 01 02 RS EG2 X 02 CG2

• Quasi-Experimental Research designs: 1) Non-Equivalent Controlled Group Design

  • Diagram: (EG) O1 X 02 (CG) 01 02
    2) Time-Series Design

  • Diagram: 01 02 03 X 04 05 06

Non-Experimental Research – Details (Expanded)

• Descriptive Research

  • Focus: description of factors/variables in natural settings; uses descriptive statistics to summarize data.

• Comparative Research

  • Focus: compare two groups to infer potential causation; not random assignment.

• Correlational Research

  • Focus: assess relationship between two variables; correlation coefficient may be used to quantify strength/direction of relationships.

Assessment (Activity Prompt)

• An activity prompts students to describe quantitative research using descriptors like:

  • employs descriptive and inferential statistics

  • distinguishes quantitative research

  • identifies aims and methods of quantitative work

Notes on Terminology and Concepts

• Quantitative research uses statistics and numerical data to test hypotheses and generalize findings.

• Qualitative research emphasizes meaning, context, and subjective experience; data are words, images, or objects.

• Triangulation involves combining qualitative and quantitative approaches to strengthen conclusions.

• The choice of approach should be driven by the research question and context, not by a rigid dichotomy.

• Common statistical tools mentioned include T-tests, Chi-square tests, and ANOVA for analyzing quantitative data.