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Research Design and Methods RCH 5301 DB V Peer Response
Unit V Assignment
Then, choose a peer(s) response and make a case for using the opposite analytical approach (e.g., If your peer(s) advocates using a test of relationships, such as simple regression analysis, to address their dilemma, make a case for using a test of differences, such as the t-test).
Peers Post
The dilemma I am going through at work is that roofers aren’t complying with safety protocols. We want to understand which factors relate to non-compliance and, if possible, identify differences between compliant and non-compliant groups. Two non-experimental strategies can help: correlational analysis and causal-comparative analysis. Each has its strengths depending on your research goals and data structure.
Correlational analysis examines the strength and direction of relationships between two or more variables without implying causation.
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Use when you have continuous or ordinal measures (e.g., hours of training, safety-climate score, frequency of toolbox talks) and want to see how they co-vary with compliance rates.
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Pros:
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Identifies key predictors for non-compliance.
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Supports multivariate models (e.g., multiple regression) to control for confounders.
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Cons:
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Cannot establish cause-and-effect.
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Sensitive to outliers and non-linear relationships.
Application: Measure each worker’s compliance score (e.g., % of tasks using fall-arrest systems) alongside potential predictors, then compute Pearson or Spearman correlations and regression coefficients to pinpoint the strongest associations.
Causal-Comparative Analytical Approach
Also known as “ex post facto,” causal-comparative compares naturally occurring groups on outcome variables without researcher manipulation.
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Use when you already have distinct groups (e.g., roofers who comply vs. those who don’t) and you want to compare their mean differences on factors like training hours or safety-culture ratings.
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Pros:
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Simple group comparisons (t-tests, ANOVA) quickly highlight significant differences.
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Useful for profiling non-compliant cohorts.
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Cons:
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Still no true causality—preexisting group differences may bias results.
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Requires clear, reliable group classification.
Application: Divide participants into “compliant” and “non-compliant” based on a threshold, then compare average training frequency or equipment availability scores between the two groups.
If the primary goal is to uncover which factors most closely relate to non-compliance—and to build predictive models—start with a correlational approach. It will help you prioritize interventions by showing which variables carry the strongest statistical ties to unsafe behavior.
If you already have a reliable way to classify roofers into compliance categories and want to profile differences, a causal-comparative approach can quickly reveal which group characteristics stand out.
Reference
Lawrence, F. P. (2023, April 20). Fall protection in roofing [PDF]. Retrieved from
• Your initial post should be at least 300 words in length.
• Your initial post should include at least one APA-formatted scholarly, professional, or textbook reference with accompanying in-text citation to support any paraphrased, summarized, or quoted material.
