PSYC424 - Research Methods - Data Screen - Missing Value Analysis - Normality Linearity and Homoscedasticity - Psychology Assignment Help

Assignment Task:

PSYC424 Research Methods Psychology Assignment Help

Question A. Missing Value Analysis 

1.    Conduct a Missing Value Analysis and identify any variables with missing data. In your MVA, include the necessary tests to determine the pattern of missing values. 

1.1.    Explain the pattern of results. That is, explain what variables include missing data, what variables require further inspection (i.e. tests of missing patterns), and whether the “missingness” in the “offending variables” is related to the scores in other variables in the data set (and in what way). Make sure to describe these patterns (i.e., in what way are they related?). Finally, please indicate any of these relationships (between missingness in a variable and scores in other variables) is significant. 

1.2.    Assume that the researcher is running a study in which the DV is self-monitoring ability. Should you conclude that the missing data are MCAR, MAR or MNAR. In your answer, make sure you make reference to the statistics you used to reach your decision. 

 
2.    Obtain descriptive statistics with all four forms of estimation (i.e. listwise, pairwise, EM and regression). 

2.1.    Look at the table “summary of estimated means”. Explain why the mean for self-esteem and emotional intelligence is larger for listwise than pairwise estimation, while the mean for the DASS subscales is smaller for listwise than pairwise estimation. Your explanation should be supported by evidence.

2.2.    Look at the correlation tables using listwise vs pairwise methods. Why is the correlation between self-esteem and DASSdep stronger for pairwise than for listwise estimation, while the correlation between self-esteem and self-monitoring ability is weaker for pairwise than for listwise estimation? 

Question B. Normality, linearity and homoscedasticity

1.    Please look at the scatterplot below and identify the relationships that may be odd-looking or may have potential problems of linearity or homoscedasticity.

1.1.    Copy and paste this plot on your answer document and circle/mark the relationships that may be a problem. Then briefly indicate what the potential problem(s) are. You may find it useful to mark the plot itself to indicate which pairwise relationships show problems of heteroscedasticity, which ones show non-linearity and which ones show both. Point out outliers if there are any.

2.    Assess normality for each of the variables in the plot above. Use both visual and statistical methods. As recommended by T&F, test significance using the zskew and zkurtosis values and not the other significance tests. Use a criterion of /z/>3.29. Note which distributions are significantly different from normal and in what direction? 

2.1.    Check for outliers using a criterion of /z/>3.29. Report any cases that are outliers according to this criterion. 
2.2.    Fix normality problems using the appropriate transformation (start with square root, move to natural log or log base 10 if needed). Obtain the necessary statistics to determine whether the normality violations were fixed. Produce a table that includes all zskew and zkurtosis values for each “offending” variable at the beginning (the ones you calculated for point 2 above), and after using transformations. Save the file that includes the transformed variables and submit it along with the output. 
2.3.    Determine whether transformations also fixed the outliers you found in 2.1, reporting their z-score value of the individual transformed cases. If this fixed the outliers, move to the next question. If it did not, then discuss what other measures you should take, if any.
2.4.    Now that the non-normal variables have been “fixed” via transformations, look at the relationships between pairs of variables again, using a matrix scatterplot like the one above but using the transformed variables where appropriate. Paste the scatterplot in your answer. Are the problems that you detected visually in 1.1 still present? To answer this question, list any potential violations that you first observed and state whether the problem was fixed for each. Indicate whether there are still problems with homoscedasticity and/or linearity in any of the pairwise relationships and describe what they are. 

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