Why Research Design Matters

• Interpretation: Knowing the design tells you what conclusions are justified.
• Evidence synthesis: Meta‑analyses, systematic reviews, guideline development.
• Critical appraisal: Spot strengths, threats to validity, and bias.

A study’s design is its blueprint; without it you can’t tell whether the walls are sound.

Why Research Design Matters (cont.)

• Design dictates inference
  • Knowing whether a study is experimental, quasi‑experimental, or descriptive tells you exactly what can and cannot be concluded.
• Read the methods first
  • The design label is usually there; if not, infer from randomization, control groups, and timing
• Use the decision tree to quickly classify unfamiliar studies
• Apply the checklist when synthesizing evidence for literature reviews or evidence‑based practice guidelines

Understanding the architecture of research lets us build stronger bridges between evidence and practice.

Randomized Controlled Trials (RCTs)

• Active manipulation of an independent variable
  
• Random assignment to conditions
  
• Often uses blinding and standardization
  
• Strong internal validity; external validity varies with setting

Interpreting RCTs

• Confirm randomization and allocation concealment
  
• Look for preregistration and protocol adherence
  
• Intention-to-treat versus per-protocol analyses
  
• Effect sizes and precision, not just p-values

Example Study

• Osuchukwu, et al. (2024). Medication safety knowledge and practice among nursing students: A parallel-group randomized controlled trial. Journal of Education and Health Promotion.
  • Parallel-group RCT of educational intervention versus standard curriculum
    
  • Outcome: medication-safety knowledge and practice
    
  • Interpretation: randomization supports causal inference about the teaching method

What Makes Them “Quasi”?

• Manipulation without full randomization
  
• Typical forms: nonequivalent groups, pretest–posttest, interrupted time series
  
• Useful when randomization is infeasible or unethical
  
• Internal validity depends on design features and covariate control

Interpreting Quasi-Experiments

• Check baseline comparability
  
• Look for matching, covariate adjustment, and sensitivity checks
  
• Examine time trends if available

Example Study

= Wen, et al. (2024). Large quasi-experimental study on an IoT smart patient-care system for fall prevention. Sensors. - Before–after implementation of a smart system; no individual randomization
- Outcome: fall incidence
- Interpretation: strong practical relevance; consider alternative explanations from nonrandom allocation

Descriptive and Correlational

• No manipulation; measure variables as they occur
  
• Estimate prevalence, distributions, and associations
  
• Cannot establish causality but can be highly generalizable

Cohort and Case-Control

• Cohort: follow a defined group forward in time
  
• Case-control: compare prior exposures between cases and matched controls
  
• Choice depends on outcome rarity, timing, and resources

Example Study (Cohort)

Fischbacher, S., et al. (2024). A prospective longitudinal cohort study of the association between nurses’ subjective and objective workload. Scientific Reports, 14, 22694.

• Four-week cohort with repeated measures per shift
  
• Multilevel models link objective workload indices to subjective load
  
• Interpretation: temporal ordering strengthens inference but remains observational

Example Study (Cross-Sectional)

• Alkouri, O., et al. (2025). Perceived stress, coping mechanisms, and influential factors among nursing students during ICU clinical placement: A cross-sectional study. PLOS ONE.
  • Single-time-point survey assessing stress and coping
    
  • Associations reported; no temporal precedence
    
  • Interpretation: useful for scope and correlates, not causes

Temporal Logic

• Cross-sectional: snapshot; faster, cheaper
  
• Longitudinal: sequences; detects change and predicts outcomes
  
• Panel and cohort variations common in nursing research

Example Studies

Cross-Sectional Study

• Alkouri, O., et al. (2025). Perceived stress, coping mechanisms, and influential factors among nursing students during ICU clinical placement: A cross-sectional study. PLOS ONE, 20(2), e0323406.
  • Surveyed nursing students during intensive care clinical rotations to assess perceived stress and coping mechanisms.
    
  • Provided a single-time snapshot of how students manage acute clinical stressors.
    
  • Illustrates the strengths of cross-sectional studies for describing prevalence and associations but also their limitation in inferring temporal or causal order.

Longitudinal Study

• Fischbacher, et al. (2024). A prospective longitudinal cohort study of the association between nurses’ subjective and objective workload. Scientific Reports, 14, 22694.
  • Followed nurses across four consecutive weeks to track both subjective workload perception and objective workload measures per shift.
    
  • Used multilevel modeling to assess within-person change and temporal relationships.
    
  • Demonstrates how longitudinal cohort designs strengthen causal interpretation by introducing temporal ordering and repeated measures.

Overview

• Compare people with an outcome (“cases”) to those without it (“controls”).
  
• Retrospective by design—looks backward to identify prior exposures or risk factors.
  
• Especially useful when outcomes are rare or take a long time to develop.
  
• Measures association, not causation; results are expressed as odds ratios.

Reading & Interpretation

• Look for how cases and controls were matched (age, sex, clinical status).
  
• Verify that exposure data were collected similarly for both groups.
  
• Evaluate recall or record-quality biases—these can distort associations.
  
• Confidence intervals around odds ratios convey both direction and strength.

Example Study

• Ayele, et al. (2024). Determinants of hospital-acquired infection among surgical patients: A case–control study. BMC Infectious Diseases, 24, 119.
  • Multicenter matched case–control design in Ethiopian surgical wards.
    
  • Identified prolonged pre-operative stay and antibiotic timing as major risk factors.
    
  • Interpretation: illustrates how careful matching and odds-ratio reporting support inference despite retrospective data.

Overview

• Use existing datasets—clinical registries, national surveys, or EMRs—to answer new questions.
  
• Cost-efficient and fast; enables very large, representative samples.
  
• Limited by how the original data were collected (variables, missingness, coding).
  
• Often observational and cross-sectional but can also model longitudinal trends.

Reading & Interpretation

• Check whether the data source and variables align with the authors’ stated aims.
  
• Assess transparency of data cleaning, inclusion/exclusion, and missing-data handling.
  
• Large N increases precision but doesn’t eliminate bias; focus on effect size and plausibility, not just p-values.
  
• Examine how the authors address confounders through regression or weighting.

Example Study

• Lee, et al. (2024). Association between nurse staffing levels and inpatient mortality using national administrative data: A secondary analysis of hospital records in South Korea. International Journal of Nursing Studies, 152, 104523.
  • Secondary analysis of 2021 hospital-admissions database covering > 500 institutions.
    
  • Found lower nurse-to-patient ratios independently predicted reduced mortality.
    
  • Interpretation: demonstrates power and limitations of large-scale administrative data for workforce research.

Purpose

• Estimate prevalence or incidence in defined populations
  
• Foundational for quality improvement and resource planning
  
• May use probability samples or census frames

Example Study

• Alrasheeday, et al. (2024). Sleep quality among emergency nurses and its influencing factors. Frontiers in Psychology.
  • Descriptive cross-sectional estimates of poor sleep prevalence
    
  • Interpretation: quantifies scope; motivates targeted interventions

Summary of Designs

Key Questions & Samples

Practical Cues

• Identify whether there is manipulation and randomization
  
• Locate timing: when variables were measured relative to each other
  
• Scan for threats and how they were handled
  
• Focus on effect sizes, intervals, and clinical meaning

Useful Resources

• CONSORT 2010 Statement
• STROBE Guidelines
• TREND Statement
• Cochrane Handbook (Chapters on RCTs & Observational Studies)