Experimental and Non-Experimental Research Designs

TRUE-EXPERIMENTAL DESIGN

Three indispensable criteria (remember the mnemonic M-R-C → Manipulation, Randomization, Control)
• Manipulation – researcher deliberately varies at least one independent variable (IV)
• Randomization – every subject has an equal, independent chance of being in any condition
• Control/Control Group – a comparison group that does not receive the intervention

Core Advantages & Significance

• Highest level of internal validity among quantitative designs
• Permits strong causal inferences because threats such as selection bias, maturation, or history are minimized through random assignment and control
• Ethical/practical implications: demands researcher responsibility for participants’ safety & informed consent when manipulating treatments (e.g., new drugs, teaching methods)

PRETEST–POSTTEST CONTROLLED GROUP DESIGN (Classic Experiment)

Structure

Random Assignment →  Group A (Experimental)  → Pre-test  → Treatment  → Post-test
Random Assignment →  Group B (Control)       → Pre-test  → Ø          → Post-test

Key Points
• Allows measurement of change within each group and difference-of-differences between groups
• Controls for pre-existing differences because pre-test scores can be used for statistical adjustment (e.g., ANCOVA)
• Main threat addressed: regression to the mean

Illustrative Example (Education)
• Population: students with similar baseline math ability
• Pre-test: standardized math skills test
• Intervention: innovative teaching method for Group A; traditional instruction for Group B
• Post-test: identical math test after the instructional period
• Analysis: compare post-test means (and/or gain scores) → if \bar X{A,post} > \bar X{B,post} significantly, infer method effectiveness

Illustrative Example (Health)
• IV = new antihypertensive drug, DV = systolic BP
• Same design logic isolates pharmacologic effect while ruling out baseline BP differences

POSTTEST-ONLY CONTROLLED GROUP DESIGN

Structure

Random Assignment →  Group A (Experimental)  → Treatment  → Post-test
Random Assignment →  Group B (Control)       → Ø          → Post-test

When to choose?
• Pre-testing is impossible, reactive, or likely to sensitize participants
• Very short interventions (e.g., one-session exposure)

Limitations
• Cannot directly verify initial group equivalence; relies entirely on randomization

Illustrative Example
• Effectiveness of a new online learning platform over one semester vs. regular in-person classes → outcome = standardized mathematics exam at semester end

SOLOMON FOUR-GROUP DESIGN

Hybrid of the previous two designs that explicitly tests for pre-test reactivity

G1:  Pre-test → Treatment → Post-test   (Pre/Post Experimental)
G2:  Pre-test → Ø        → Post-test   (Pre/Post Control)
G3:  Ø       → Treatment → Post-test   (Post-only Experimental)
G4:  Ø       → Ø        → Post-test   (Post-only Control)

Analytical Logic
• Treatment effect: compare G1 vs G2 and G3 vs G4
• Pre-test effect: compare G2 vs G4 (controls) or G1 vs G3 (experimentals)
• Interaction (Pre-test × Treatment): whether taking the pre-test changes responsiveness to treatment

Advantages
• Most comprehensive safeguard against testing effects while still permitting baseline measurement
• Enhances external validity – results generalize to settings where no pre-test is given

Example (Reading Comprehension)
• Two experimental groups receive the new method, two control groups receive traditional instruction
• Only half receive a baseline reading test
• Post-test comprehension scores analyzed via 2×2 ANOVA (Pre-test × Treatment)

DECISION TREE: IS MY STUDY TRUE, QUASI OR NON-EXPERIMENTAL?

• Manipulation? → if no, design is Non-Experimental
• Randomization? → if yes & there is manipulation → True Experimental; if no → Quasi-Experimental
• Control Group? → if manipulation & randomization exist but no control, study slips to Pre-Experimental

Mnemonic “\textbf{M} \rightarrow \textbf{R} \rightarrow \textbf{C}” (Manipulation, Randomization, Control) must all be YES to claim true experiment

NON-EXPERIMENTAL (DESCRIPTIVE) RESEARCH DESIGNS

Purpose: observe, describe & document phenomena as they naturally occur; often a springboard for hypotheses

1. SURVEY RESEARCH

Core idea: gather self-report data from samples to infer characteristics of a population

a. Cross-Sectional

• Data collected once at a single time-point
• Efficient, economical; ideal for prevalence estimates & group comparisons
• Examples: health survey of risk factors; election opinion poll; year-end academic benchmarking across grades

b. Longitudinal

• Same respondents measured repeatedly across time
• Tracks development, stability, causal ordering
• Greater cost & attrition risk
• Variants: cohort study, panel study, trend study
• Examples: birth cohort followed to adulthood; annual survey of the same voters; K-to-college academic trajectory tracking

Cross-Sectional vs Longitudinal – Key Contrasts
• Time frame: snapshot vs motion picture
• Purpose: comparison/association vs growth/change/causality
• Resources: longitudinal needs more , time & logistics

2. CORRELATIONAL RESEARCH

Goal: quantify direction & strength of association between variables without experimental manipulation

a. Bivariate Correlation

• Two variables, one coefficient (e.g., Pearson’s r, Spearman’s \rho)
• Example 1 – Study time vs GPA; Example 2 – Education level vs Income

b. Prediction Studies

• Use known data to forecast future outcomes via statistical/algorithmic models
• Classic field: weather prediction (inputs = temp, pressure, satellite imagery → outputs = tomorrow’s forecast)

c. Multiple Regression Prediction Studies

• Simultaneously evaluates multiple predictors X1, X2, …, Xk for one DV Y • Example – Predicting grades using study hours, attendance, prior GPA, SES → Y = \beta0 + \beta1X{study} + \beta2X{attend} + \beta3X{priorGPA} + \beta4X{SES} + \epsilon$$

3. EX-POST FACTO / CAUSAL-COMPARATIVE

• After-the-fact examination of naturally occurring IVs
• Researcher cannot manipulate the IV – instead selects groups that already differ
• Approximates causal inference but vulnerable to confounders
• Classic example: Smoking vs Lung Cancer – groups formed based on smoking history then outcomes compared retrospectively
• Additional examples: SES impact on test scores; prenatal smoking on birth weight; early childhood experience on adult attachment

4. COMPARATIVE DESIGN

• Compares two or more groups on one or more variables at a single point
• Education – compare effectiveness of teaching methods; Psychology – stress levels in urban vs rural teens

5. EVALUATIVE RESEARCH

• Systematic assessment of a program, policy or institution → Did it work?
• Example – Government job-training program: employment rates, income, satisfaction of participants vs non-participants

6. METHODOLOGICAL STUDIES

• Focus on developing, validating or refining instruments/methods
• Often integrates multi-disciplinary data to create scale-matched tools

QUANTITATIVE RESEARCH: STRENGTHS & WEAKNESSES (OVERVIEW)

Strengths

• Objectivity – numerical data minimize researcher bias
• Clearly defined research questions & structured instruments
• Allows sophisticated statistical analysis (e.g., ANOVA, regression, SEM)
• Large samples → generalizability; results often replicable
• Useful for hypothesis testing & predicting future outcomes
• Data can be processed quickly & efficiently with modern software

Weaknesses

• Requires large N → costly & time-intensive
• Contextual richness may be lost; human experience reduced to numbers
• Data collection can be difficult (non-response, missing values); small procedural glitches jeopardize validity
• Ethical considerations when manipulating variables (e.g., withholding beneficial treatment)

QUICK REFERENCE – TAXONOMY OF DESIGNS

• Experimental
– True-Experimental (Pretest-Posttest, Posttest-Only, Solomon 4)
– Quasi-Experimental (manipulation + control, no randomization)
– Pre-Experimental (manipulation without control/randomization)
• Non-Experimental / Descriptive
– Survey (Cross-Sectional, Longitudinal)
– Correlational (Bivariate, Prediction, Multiple Regression)
– Ex-Post Facto (Causal-Comparative)
– Comparative
– Normative / Evaluative
– Methodological

STUDY DESIGN SELECTION – ETHICAL & PRACTICAL CONSIDERATIONS

• Feasibility: Can we ethically manipulate the IV? (e.g., cannot randomly assign smoking)
• Control vs Realism: True experiments maximize control but may sacrifice naturalism; surveys maximize ecological validity but lack causal certainty
• Resource Availability: Time, budget, participant access shape choice between cross-sectional vs longitudinal, simple vs Solomon design
• Validity Trade-offs: balancing internal (experimental control) and external (generalizability) validity