Practical Research 2 – Quantitative Research Comprehensive Notes
Definition of Research and Truth
Research = systematic study aiming to know, understand, and establish TRUTH.
Involves Higher-Order Thinking Skills (HOTS) → intensive, critical, spiral process.
Generates knowledge that can inform policy formulation & enhancement.
TRUTH
Anything accepted as reality or fact.
Objective Truth → universally valid (e.g., empirical facts).
Subjective Truth → valid only for a specific group or individual (e.g., opinions).
Purpose of Research
\textbf{Explore} – Identify new phenomena, patterns, or variables.
\textbf{Describe} – Provide detailed, systematic portrayals of a phenomenon.
\textbf{Explain} – Clarify causal mechanisms, relationships, or underlying principles.
Characteristics of the Scientific Approach
Tentative → findings are open to constant review & revision.
Empirically verifiable → claims must be backed by observable, measurable evidence.
Ethically neutral → safeguards respondents’ confidentiality, privacy, and well-being; evaluates potential harm/risks.
Shared & public → results disseminated for scrutiny, replication, and application.
Quantitative Research Overview
Focus: Gathering quantifiable data & applying statistical techniques.
Answers questions with definite, measurable responses.
Seeks patterns that can be generalized to broader populations.
Core Characteristics of Quantitative Research
Objectivity
Data collection & analysis performed with minimal researcher bias.
Larger Sample Size
Enhances reliability & statistical power; offsets individual respondent bias.
Visually Presentable Outputs
Findings often summarized through graphs, charts, and tables for clearer interpretation.
Faster Data Analysis
Statistical software enables rapid processing once data are coded.
Generalizable Data
Results can be inferred to the target population when sampling is appropriate.
Faster Data Collection (with standardized instruments)
Surveys, questionnaires, or sensor logs streamline gathering.
Participants Limited to Pre-Set Responses
Ensures consistency but restricts depth of expression.
Cost Implications of Large Samples
Increased recruitment, incentives, and logistical expenses.
Valid Data Collection
Well-designed instruments capture exactly the variables intended.
Reliability of Results
Consistency assessed through techniques such as test-retest or Cronbach’s \alpha.
Pilot testing typically requires \ge 30 respondents.
High Replicability
Clear procedures allow other researchers to repeat studies, verify, and extend findings.
Predictive Ability
Statistical models (e.g., regression) can infer relationships and forecast outcomes.
Disadvantages & Limitations
Provides limited depth for exploring nuanced human experiences.
May omit contextual factors that defy numerical description.
Rigid design → difficult to adapt mid-study without compromising validity.
May lack qualitative richness necessary for comprehensive explanations.
Triangulation Concept
Combining multiple approaches to offset individual method weaknesses.
Qualitative → in-depth, contextual insight.
Quantitative → breadth, measurability, generalization.
Mixed (Quanti + Quali) → integrates strengths of both.
Key Concepts: Variables
Variable = any attribute possessing quantity or quality that can vary.
Variation → differences observed within a class of objects.
Classification by Nature
Quantitative Variable
Expressed numerically; analyzed along a continuum (e.g., height in cm).
Categorical Variable (Qualitative)
Represent distinct categories without inherent numeric order (e.g., blood type).
Classification by Role in Analysis
Independent Variable (IV)
Presumed cause or predictor.
Dependent Variable (DV)
Presumed outcome or effect.
Basic Quantitative Research Designs
Descriptive → portrays current conditions without manipulating variables.
Comparative → examines significant differences between groups.
Correlational → explores relationships/associations between two or more variables.
Experimental → manipulates IV(s) to observe causal impact on DV(s).
Hypothesis Formulation
Testable statement predicting study outcomes.
Null Hypothesis: posits no difference or relationship (e.g., “There is no relationship between study hours and exam score”).
Alternative Hypothesis: posits a difference or relationship (e.g., “Increased study hours relate positively to exam score”).
Sampling Considerations
Larger sample (often \ge 100) increases statistical power & representativeness.
Pilot test (≈ 30 respondents) assesses instrument clarity, reliability, validity.
Formulating a Working Title
Should concisely summarize main idea(s); usually the first element readers view.
Formula: Cause + Effect + Respondents & Location
Example: “Time Management Skills and Academic Performance of Senior High Students in Metro Cebu”.
Statement of the Problem (SOP) Template
General Objective
Broad, overarching goal of the study.
Specific Objectives (or Specific Questions)
Itemized, measurable tasks derived from the general objective.
Often aligned with variables (e.g., determine significant relationship, compare group means, identify predictors).
DISSECTING A TITLE
identify the cause of the problem
Identify the effect of the problem
Who are the respondents?
Combine cause and respondents
Combine effect and respondents
Combine cause and effect