A discussion question using IBM SPSS regarding multiple regression, statistics homework help

User Generated

nqf93

Mathematics

Quantitative Reasoning

Walden University

Description

Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.

To prepare for this Discussion:

  • Review this week’s Learning Resources and media program related to multiple regression.
  • Create a research question using the General Social Survey that can be answered by multiple regression. To access the dataset, please click on the following link https://class.waldenu.edu/bbcswebdav/institution/U... and the username is akkissia.slay@waldenu.edu and the password is Pitbull93! The course is Quantitative Reasoning and we are in week 9.

By Day 3

Use SPSS to answer the research question. Post your response to the following:

  1. What is your research question?
  2. What is the null hypothesis for your question?
  3. What research design would align with this question?
  4. What dependent variable was used and how is it measured?
  5. What independent variable is used and how is it measured?
  6. What other variables were added to the multiple regression models as controls?
  7. What is the justification for adding the variables?
  8. If you found significance, what is the strength of the effect?
  9. Explain your results for a lay audience, explain what the answer to your research question.

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Explanation & Answer

Find the attached document,Best Regards.

1
Running Head: MULTIPLE REGRESSION ANALYSIS

Multiple Regression Analysis
Student’s Name
Institutional Affiliation

2
MULTIPLE REGRESSION ANALYSIS

Research question
Does the number of hours worked in a week, level of education and hours watching TV per day
of an individual have a significant impact on income?

Null hypothesis
Individual’s income cannot be explained by an individual’s level of education, hours worked per
week and hours of watching TV per day.
Research design
This is a correlational study design. According Slavin (2012), Correlational designs are used
when we have two or more quantitative variables from the same group of subjects, and we intend
to determine if there is a relationship between the two or more variables. This is the same
scenario we have here.
Dependent variable and how is it measured
The dependent variable is respondent’s income. This is a metric variable and is measured in an
interval scale.
Independent variable and how is it measured
The independent variable is Number of Hours Usually Work a Week. It is a metric variable
measured on an interval scale.

3
MULTIPLE REGRESSION ANALYSIS

Control Variables
Highest school level attained and the number of hours spent daily watching TV was used in the
model as control variables.
Justification
According to Scandura (2012), Control variables help in observing the effect of the dependent
variable by observing the independent variables one at a time. When dealing with the variables in
the model, the assumption is that when one of the predictors is being analyzed or considered,
then the others are assumed to remain constant. The analyst assumes that the predictors do not
have any impacts on the variables and therefore it is possible to get a clear image of the impacts
of that one variable. The two control variables which are hours watching TV per day and level of
education relate to the dependent variable which is income. We however want to observe the
effect predictor, holding the Control variables constant.
Significance and strength of the effect
The overall regression model is significant from the ANOVA output (P=0.005). This means that
there is enough evidence against the null hypothesis and thus it is rejected. We thus conclude that
an individual’s income can be explained by the individual’s level of education, hours worked per
week and hours of watching TV per day, and thus R Square can be interpreted.
The results of the summary of the model show that the R Square value after adjustment is 0.483.
This means that, about 43% of the variation in income is explained by the ...


Anonymous
I was struggling with this subject, and this helped me a ton!

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