To make meaningful causal inferences when conducting quantitative analyses, one must employ...?
A. Dummy coding when using regression models
B. Parametric methods of analysis robust in relationship to violation of basic assumptions
C. T-Tests and Analysis of Variance methods
D. Random assignment of participants to treatment and control conditions
The students at Littlewood Regional High School cut an average of 3.3 classes per week. A random sample of 117 seniors averages 3.8 cuts per week, with a standard deviation of 0.53. Are seniors significantly different from the student body as a whole?
To make meaningful causal inferences when conducting quantitative analyses, one must employ random assignment of participants to treatment and control conditions. This is because random assignment helps minimize the influence of confounding variables, ensuring that any observed effects can be attributed to the treatment rather than other factors.
To implement random assignment, researchers randomly assign participants to different treatment groups or conditions. This helps create comparable groups at the outset, reducing the likelihood of systematic differences between the groups that could affect the results.
Dummy coding, parametric methods of analysis, and T-Tests and Analysis of Variance methods are all important aspects of quantitative analyses, but they alone are not sufficient for establishing causal relationships. Dummy coding is a technique to represent categorical variables in regression models, parametric methods help make appropriate assumptions and handle violations, and T-Tests and ANOVA methods are used to compare means between groups. However, these methods are not designed to address issues of causality.
Therefore, the correct answer is D. Random assignment of participants to treatment and control conditions.