Nature of Inquiry and Research - Comprehensive Study Notes

Nature of Inquiry and Research

  • Origin and scope

    • Inquiry vs Research
    • Inquiry: art of questioning in order to gather information or evidences to explain a certain condition, situation, or event in life.
    • Research: creative and systematic work undertaken to establish or confirm facts, reaffirm results of previous work, solve new or existing problems, support theorems, or develop new theories; seeks information to have extensive knowledge.
    • Relationship: Inquiry is the process of asking questions and gathering information; research is the systematic and rigorous work that uses inquiry to build knowledge.

  • Etymology and definition of research

    • The word research comes from Middle French "recherche" meaning "to go about seeking"; from Old French "recerchier" (re- + cerchier/sercher) meaning to search.
    • Research is a careful and systematic study and inquiry in some field of knowledge.
    • It is an investigation of a phenomenon or the results of previous studies to find out their present relevance.
    • Prefix re- (again) implies reviewing, reinvestigating, or reexamining what was searched for.
    • The outcome of research is a vital tool to develop the research study.

  • The linguistic and practical roots of research

    • Linguistic origin: re + search → again/review; emphasizes revisiting and refining knowledge.
    • Practical aim: generate new knowledge, verify or extend existing knowledge, and provide a basis for action, policy, or further inquiry.

Two Broad Categories of Research

  • Quantitative Research

    • Definition: a type of inquiry where relations are established through the collection of numerical data analyzed to derive generalizations.
    • It is a systematic scientific analysis of data and their relationships.
    • Key contrast: numerical, structured, instrument-based, and statistical in nature.

  • Qualitative Research

    • Definition (in context): typically seeks information to gain extensive knowledge, often involving non-numerical data such as words, impressions, and meanings.
    • Not explicitly enumerated in every slide, but implied as the counterpart to quantitative research in the two-category scheme.

Quantitative Research: Characteristics, Strengths, Weaknesses, and Kinds

  • Characteristics (core features)

    • OBJECTIVE: Data gathering and analysis are done accurately, objectively, and are not biased by the researcher’s intuition or guesses.
    • LARGE SAMPLE SIZE: To obtain more meaningful statistical results, data come from a large sample size.
    • GENERALIZABLE & RELIABLE DATA: Data from a sample can be applied to the population if sampling is proper (sufficient size and random sampling).
    • FAST DATA COLLECTION: Uses standardized instruments to gather data from large samples efficiently.
    • FAST DATA ANALYSIS: Statistical tools enable quick analysis.
    • VISUAL RESULT PRESENTATION: Numerical data allow graphs, charts, and tables for clear interpretation.
    • REPLICATION: The method can be repeated to verify findings, enhancing validity.
    • INTERVENTION/EXPERIMENTAL CAPABILITY: Can test cause-and-effect relationships under controlled conditions.

  • Strengths

    • Objectivity and reduction of researcher bias.
    • Generalizability of findings to a population when sampling is appropriate.
    • Ability to predict outcomes using numerical data.
    • Efficient handling of large datasets with statistical software (e.g., SPSS).
    • Clear visualization of results through graphs and tables.
    • Replicability and verification of findings.
    • Efficiency in data gathering and analysis.
    • Established validity and reliability when well designed.

  • Weaknesses

    • Lacks depth in explaining human experiences and contextual nuances.
    • Some phenomena (feelings, beliefs) resist numeric quantification.
    • Respondents may provide constrained or artificial responses due to fixed-choice formats.
    • Large sample sizes can be costly and time-consuming.
    • Limited to what can be measured by instruments; may miss unanticipated factors.

  • Kinds (research designs under quantitative umbrella)

    • Non-Experimental
    • Descriptive Research Design
      • Purpose: describe a phenomenon as it occurs in nature; no experimental manipulation; no initial hypothesis.
      • Structure: 1 variable and 1 group/population.
      • Example: Number of hours Grade 12 learners spend on social media.
    • Correlational Research Design
      • Purpose: identify relationships between variables; does not establish causation.
      • Structure: 2 variables and 1 group/population.
      • Example: Parental involvement and academic achievement.
    • Ex Post Facto (Causal-Comparative) Research Design
      • Purpose: investigate possible causal relationships between previous conditions and present outcomes without experimental manipulation.
      • Structure: 1 independent variable with 2+ groups.
      • Example: Attitudes toward a program among different student groups.
    • Quasi-Experimental Research Design
    • Purpose: establish cause-and-effect relationships with some manipulation but without random assignment.
    • Notes: Less internal validity than true experiments due to non-random assignment.
    • Experimental Research Design
    • Purpose: establish causal relationships with random assignment and controlled manipulation.
    • Notes: More conclusive; subjects randomly assigned to experimental conditions.

  • Interventions and designs

    • Intervention Design (Experimental Control Group with Random Assignment) vs Non-Experimental vs Quasi-Experimental vs Experimental: a summary table in the slides shows which features (Intervention, Random Assignment, Group) apply to each design type (the slide indicates Yes/No for each attribute).

Descriptive, Correlational, Ex Post Facto, Quasi-Experimental, and Experimental Designs Details

  • DESCRIPTIVE RESEARCH DESIGN

    • Observes a phenomenon as it occurs in nature without manipulation.
    • Does not start with a hypothesis; explores characteristics and attributes.
    • Typical structure: one variable and one group/population.
    • Example: Hours spent on social media by Grade 12 learners.

  • CORRELATIONAL RESEARCH DESIGN

    • Identifies relationships between variables.
    • Data collected by observation; does not imply causation.
    • Typical structure: two variables and one group/population.
    • Example: Parental involvement and academic achievement of Grade 12 learners.

  • EX POST FACTO OR CAUSAL COMPARATIVE RESEARCH DESIGN

    • Investigates possible causal relationships between previous conditions and present outcomes.
    • No experimental manipulation; uses existing groups.
    • Typical structure: one variable and 2+ groups.
    • Example: Attitudes toward practical research among different student groups.

  • QUASI-EXPERIMENTAL RESEARCH DESIGN

    • Establishes cause-and-effect with incomplete randomization.
    • Independent variable identified but not manipulated; pre-existing groups may be used.
    • Example: The effect of part-time employment on the achievement of high school students.

  • EXPERIMENTAL RESEARCH DESIGN

    • Establishes cause-and-effect with random assignment and manipulation of the independent variable.
    • Example: The effect of teaching with a cooperative group strategy vs traditional lecture on student achievement.

  • INTERVENTION DESIGN (summary)

    • Compares experimental and control groups with random assignment to determine the effect of a treatment.
    • Distinguishes among Experimental, Non-Experimental, Quasi-Experimental based on randomization and manipulation.

Identifying Research Designs: Examples and Applications

  • Example: Autism severity and others’ helping behaviors

    • Design: Correlational (relationship between severity and helping behaviors; causation not established).

  • Example: Youth cosmetics preferences

    • Design: Descriptive (characteristics and preferences of a population at a point in time).

  • Example: Reading ability after a special program for speech disability

    • Design: Ex Post Facto / Causal-Comparative (treatment vs control without random assignment; assess reading ability after program).

  • Example: Evaluation of K to 12 program six years from today (cost, efficiency, impact on quality)

    • Design: Evaluation Research (assess program effectiveness and implications over time).

  • Example: Teacher tests a new teaching strategy vs traditional method

    • Design: Experimental (randomized groups and testing outcomes).

Activities and Practice Items (Overview)

  • Activity 1: Finding clues

    • Task: Group clues into Quantitative Research (Box A) vs Qualitative Research (Box B).
    • Examples seen in the slides include: Measurable, Statistical, Objective, Intervention, Experimental group (Box A); Narrative, Text-based, Unstructured observation, Inductive (Box B).

  • Activity 2: Matching quantitative research titles to designs

    • Task: Match given titles to research designs (Experimental, Descriptive, Ex post facto, Quasi-experimental, Correlational, Case Study).
    • Example titles provided cover effects of eggplants on mice (experimental), factors affecting job satisfaction (descriptive or correlational depending on design), prevalence of domestic violence during COVID-19 (descriptive or correlational depending on data), effects of age on social media platform choice (correlational or descriptive), relationship between intelligence and sports choices (correlational).

Word unscramble: Identifying Key Terms (Activity 1.1)

  • The activity provides a list of scrambled terms that correspond to common quantitative/experimental design concepts. Examples (unscrambled):
    • Comparative researches
    • Descriptive researches
    • Experimental designs
    • Non-experimental researches
    • Correlational researches
    • Experimental designs (repeated)
    • Quantitative researches
    • Qualitative researches
    • etc.

Practice: Determining Quantitative Design for Sample Titles (Pages 42–43)

  • Students practice classifying titles into a quantitative research design (Experimental, Descriptive, Ex post facto, Quasi-experimental, Correlational) and provide justification.

  • Example prompts include:

    • Relationship between Academic Stressors and Learning Preferences of Public Senior High School Students in a given area → likely Descriptive or Correlational depending on data collection and aims.
    • Reading Electronic Learning Materials as a Support for Vocabulary of Grade 1 Pupils → Descriptive.
    • Effects of Morning Exercise on Health Anxiety Level of Senior Citizens → Descriptive or Correlational.
    • Measuring Gadgets Usage of Grade 11 Students at Home during Covid Community Quarantine → Descriptive/Correlational.
    • Level of Academic Achievement of Senior High Schools in Balanga in Different Learning Modalities → Descriptive (and possibly Comparative, depending on design).

  • Additional tasks (Page 43) require identifying the appropriate design for research titles and offering brief justification.

Practice: True/False–Style Items (Page 44)

  • Check whether each statement describes characteristics of Quantitative Research: 1) Quantitative research can be based on replication (e.g., replicating a previous study with new populations).
    • True
      2) In quantitative research, a sample needs to be large enough to adequately represent the population.
    • True
      3) Quantitative research includes interview data described in a narrative that points out themes and trends.
    • False (this describes qualitative analysis)
      4) Quantitative research values depth of meaning and people’s subjective experiences and their meaning-making processes.
    • False (this describes qualitative emphasis)

Formulas and Numerical References

  • Slovin’s formula for determining sample size (n) given population size (N) and margin of error (e):
    • n = \frac{N}{1 + N e^{2}}
    • Where:
    • N = population size
    • e = margin of error
    • n = sample size

Additional Context and Practical Relevance

  • Real-world relevance

    • Quantitative methods provide scalable, generalizable findings useful for policy, curriculum design, and program evaluation in education settings (e.g., impact of teaching strategies, student engagement correlates, and program effectiveness).
    • Qualitative and mixed methods complement by offering depth, context, and explanations for observed patterns.

  • Ethical and methodological considerations

    • Randomization and control enhance internal validity but may be constrained by practical or ethical concerns in educational settings.
    • Large samples improve generalizability but increase cost and logistics.
    • Instrument validity and reliability are critical to ensure trustworthy data.

  • Foundations and connections

    • The module connects to foundational principles of research design, measurement, and analysis: objectivity, sampling, measurement, inference, and the trade-offs among descriptive, correlational, ex post facto, quasi-experimental, and experimental designs.

  • Quick reference glossary

    • Inquiry: questioning and information gathering to explain phenomena.
    • Research: systematic study to establish facts and develop knowledge.
    • Descriptive: describe a phenomenon as it occurs.
    • Correlational: identify relationships between variables.
    • Ex post facto: study potential causal relationships using existing groups.
    • Quasi-experimental: causal inference with non-random assignment.
    • Experimental: rigorous causal inference with random assignment.
    • Population: the entire group of interest.
    • Sample: a subset of the population.
    • Instrument: tool used to collect data (survey, test, etc.).
    • SPSS: statistical software commonly used for quantitative data analysis.

Endnotes

  • The slides include several recurring timer/YouTube promotional blocks that are not central to the methodological content and can be ignored when studying the core concepts.