Levels of Measurement, Basic OLS Regression Questions

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• lomaxbridges
In summary, the model 5 results suggest that there is no significant relationship between course delivery method and student performance.
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Examine the variables inModel 5, Table 2:

Identify the variables by name and specify the level of measurement (nominal, ordinal, interval, ratio) under each of the categories below.

You have several variables that are under Independent and Control-please choose one or the other or explain why you believe we could consider a variable either or both. Note that Continuous is not synonymous with interval/ratio.

Independent Variable(s):
Name Level of Measurement

Course Type Nominal
Student GPA Numeric (continuous)
Year in school (senior) Nominal
Online courses taken Numeric (continuous)discrete
Like working with others Ordinal
Instructor interaction Ordinal
Gender (male) Nominal
Credit hours Taken Numeric (continuous)
Hours worked Per Week Numeric (continuous)

Dependent Variable:
Name Level of Measurement

Student scores Numeric (continuous)

Control Variable(s):

Name Level of Measurement

Student GPA Numeric (continuous)
Year in school (senior) Nominal
Online courses taken Numeric (continuous)
Like working with others Ordinal
Instructor interaction Ordinal

Yes, but note we are treating them as interval/ratio in the analysis

Gender (male) Nominal
Credit hours taken Numeric (continuous)discrete
Hours worked per week Numeric (continuous)

In a couple sentences describe how you differentiated between the IVâs and CVâs.

Independent variables are the variables that are changed or controlled during the experiment.

In general, this is correct but note that this isnât an experiment where researchers manipulate the variable. It is a quasi-experiment. These variables vary across individuals but not because of anything the researcher does. He or she does not assign the students to a classroom or online. All of the predictor variables vary across individuals, not just the IVs. So you need another way to evaluate what the IVs are in this case.

10. Summarize what the results presented in Table 2, model 5 suggest about the association between course delivery method and student performance? What additional insights can we draw about this relationship when we compare Model 5 with Model 1?Explain your answer.

In model 5, the p-value for course type is not significant after adjusting the controlled variables which suggest that there is no significant association between course type and student scores. Student GPA and Instructor interaction are the only two independent variables that are associated with the student scores.

Be more specific about what it means when a coefficient on a variable is reduce (in this case to non-significance) when another variable(s) is included in subsequent models. Review your notes on mediation and apply to the variables in the article.

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I figured out the first part but I still need help with number 10 if anyone is there lol

What are the different levels of measurement?

The four levels of measurement are nominal, ordinal, interval, and ratio. Nominal data are categories with no numerical value, ordinal data have a specific order but no consistent numerical difference between categories, interval data have consistent numerical differences but no true zero point, and ratio data have a true zero point and consistent numerical differences.

What is the purpose of OLS regression?

OLS (ordinary least squares) regression is a statistical method used to analyze the relationship between a dependent variable and one or more independent variables. Its purpose is to determine the best fitting line or curve to describe the relationship between the variables and make predictions based on that relationship.

What are the assumptions of OLS regression?

The main assumptions of OLS regression are linearity, independence of errors, homoscedasticity (equal variance of errors), and normality of errors. These assumptions must be met for the results of the regression to be valid.

How do you interpret the coefficient of determination (R-squared) in OLS regression?

R-squared is a measure of how much of the variation in the dependent variable is explained by the independent variables in the regression model. It ranges from 0 to 1, with higher values indicating a better fit of the model to the data. However, it does not indicate causation and should be interpreted in conjunction with other measures and the context of the study.

What is multicollinearity and how does it affect OLS regression?

Multicollinearity occurs when independent variables in a regression model are highly correlated with each other. This can lead to inflated standard errors and unreliable estimates of the coefficients. It is important to check for multicollinearity and address it if present before interpreting the results of an OLS regression.

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