What are the indicators for using a regression analysis? Create a research scenario in which it would be correct to use a regression analysis, including the research question, sample size, and dependent and independent variables? PLEASE ADD IN-TEXT CITATION AND REFERENCE THIS IS NOT AN ESSAY, JUST ANSWER TH EQUESTION AND INCLUDE A SCENERIO Purchase the answer to view it
Indicators for using a regression analysis is determined by the nature of the research question and the relationship between the dependent and independent variables. Regression analysis is commonly employed when examining the association between variables and studying the impact of independent variables on a dependent variable. It allows researchers to determine the strength, direction, and significance of this relationship.
One key indicator for using regression analysis is when there is a hypothesis that a dependent variable can be explained or predicted by one or more independent variables. By examining the relationship between these variables, researchers can gain insights into the factors that influence the outcome of interest. Additionally, regression analysis is useful when the goal is to quantify the impact of independent variables on the dependent variable, by estimating the coefficients or slopes associated with these variables.
Another indicator for using regression analysis is when there is a continuous dependent variable. Regression analysis is well-suited for analyzing continuous data, as it models the relationship between variables based on a continuous scale. This enables researchers to examine the relationship between variables that are not limited to specific categories or levels, allowing for a more nuanced analysis of the relationship.
Additionally, regression analysis is appropriate when there is a need to control for potential confounding variables and assess the unique contribution of each independent variable. By including multiple independent variables in the analysis, researchers can determine their individual impact on the dependent variable, while controlling for other variables that may influence the outcome. This enables a more accurate understanding of the relationship between the variables of interest.
Consider a research scenario in the field of psychology examining the relationship between sleep duration and academic performance in college students. The research question is: “To what extent does sleep duration predict academic performance in college students, after controlling for other relevant factors?”
In this scenario, the sample size would consist of a representative group of college students. The dependent variable would be academic performance, which could be measured using the cumulative grade point average (GPA) or standardized test scores. The independent variable would be sleep duration, measured in hours. Other potential independent variables to control for might include gender, study habits, and extracurricular activities, as these factors may also influence academic performance.
By conducting a regression analysis, the researchers could determine the relationship between sleep duration and academic performance, while taking into account the influence of other variables. The analysis would involve estimating the regression coefficients or slopes associated with sleep duration, which would indicate the strength and direction of the relationship. The statistical significance of these coefficients would also be assessed to determine if the relationship is statistically significant.
In this research scenario, regression analysis would be appropriate because the dependent variable is continuous (e.g., GPA) and the researchers are interested in quantifying the impact of sleep duration (independent variable) on academic performance (dependent variable), while controlling for other potential confounding variables. By conducting this analysis, the researchers would be able to determine the extent to which sleep duration predicts academic performance in college students, after accounting for other factors that may influence academic success.
In conclusion, regression analysis is indicated when there is a hypothesis about the relationship between variables, particularly when there is a continuous dependent variable and the need to control for potential confounding variables. In the provided research scenario, regression analysis would be appropriate to examine the extent to which sleep duration predicts academic performance in college students, after considering other relevant factors. By conducting this analysis, researchers can gain insights into the relationship between sleep duration and academic performance, contributing to our understanding of factors that influence student success.
References:
– Stevens, J. (2009). Applied multivariate statistics for the social sciences. Taylor & Francis.