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ANLY 500: Principle Of Analytics - Data Screening - R Studio Assignment Help
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Assignment Task: You would be analyzing this data to see if there is an interaction between gender and other information provided on several variables. You want to screen the whole dataset at once to look for issues, since all the variables will be used in several different hypotheses. Include the appropriate output into this document while answering the questions, and include your R syntax in your blackboard submission. The participant was asked to imagine they were interviewing a person for a job. Participant gender was recorded, and then each participant was randomly assigned to an “information” group, where different résumés were given to each group (see below). Participants were then allowed to talk to the job candidate (a researcher in disguise) for five minutes, and finally, completed several questionnaires. Here are the variable descriptions:- Gender – each participant’s gender.
- Information – which category each subject was assigned. Information poor participants were only given a few pieces of information about a person (short résumé), while information rich participants were given more information about a person (long résumé).
- Likability – each participant rated how likeable a person was given some information and a short time talking to them (scale is in percentages).
- Externalatt – Explicit rating of a participant’s attitude assessed by asking the participant how they felt about the person on a 9 point Likert scale.
- Internalatt – Implicit rating of a participant’s attitude about a person assessed by the implicit attitudes test on a 9 point Likert scale.
- Ovcompt – a rating scale about how much a participant thought the participant was overcompensating for a flaw on their résumé on a 0-20 scale.
- Selfesteem – an average rating of each participant’s self esteem on a 0-10 scale.
- Negmood – a rating of how negative/positive a participant was during the fake interview where lower numbers are more positive on a 0-10 scale.
- Check the data for out of range scores.
- Include a summary showing you do/do not have out of range scores.
- If necessary, fix the out of range scores.
- Indicate what the problems were in the dataset.
- Make all out of range values NA/missing.
- Include a summary showing that you fixed the accuracy issues.
- Fix the factored columns to have nice labels (i.e. Proper Case, Fully Spelled out).
- Use the factor command to change the labels that already exist into better labels.
- Include a table of each of those columns to show that you fixed the labels. This command will also help you make sure you didn’t accidentally delete the column.
- What type of missing data do you appear to have?
- Try clicking on the dataset to open in in the scripting window to view it. Does it appear to be random? Or did everyone skip the same question?
- Include a summary of the missing data by participant.
- This dataset is too small to properly estimate for each participant (i.e. any missing data point puts them over the 5% rule). However, for practice, we are going to estimate any participants who have less than 20% missing data.
- Include a table of the missing data by column, after excluding participants with too much missing data.
- Use mice to impute the continuous data.
- Include a summary of the data that shows that you might have NA values for categorical columns but not continuous columns.
- Calculate Mahalanobis distance scores for your data.
- What is your df for the cut off score?
- What is the cut off score?
- How many outliers did you have? You can include the summary of the mahal < cutoff.
- Delete the outliers.
- Include a symnum table of the continuous variables.
- Are any of the variables too highly correlated?
- Include the multivariate normality histogram.
- Interpret the graph. Does it indicate multivariate normality?
- Include the multivariate QQ plot.
- Interpret the graph. Does it indicate multivariate linearity?
- Include the multivariate residuals plot.
- Interpret the graph. Does it indicate homogeneity?
- Interpret the graph. Does it indicate homoscedasticity?
- Result section style (APA and AMA):
- Double space
- Times New Roman 12 point
- Two decimals
- Centered, bolded Results
- Short description of the study/variables.
- Accuracy – did you have problems? What did you do to fix it?
- Missing data - did you have problems? What did you do to fix it?
- Outliers - did you have problems? What did you do to fix it?
- Additivity – did you have any issues?
- Assumptions:
- Normality
- Linearity
- Homogeneity
- Homoscedasticity
- On each, did you meet the assumption / have problems?
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