Examining your data
4 important questions on Examining your data
Before application the nature of the data and the relationships must be examined and explored. What is the difference in assessing the characteristics in univariate, bivariate and multivariate variables?
Bivariate; This is about the distribution of two variables assessed in relation to one another. You can assess this in a scatterplot (data points).
Multivariate; Portrayel of the data values.
When is missing data problematic and what might be the two causes of missing data?
Missing data can be caused by the collection process or by answers of individual respondents.
What is the process of assessing missing data? (4 steps) Also include the statistical information needed to draw the conclusions in each step.
2. Determine the extend of missing data; is action needed?
3. Diagnose the randomness: is the missing data random (MCAR) or not random (MAR)? (in SPSS, using Little MCAR test, if NO significant differences are found, then the missing values are MCAR).
Random data does not inquire bias, whereas not random (MAR) data biases the results.
4. If MCAR, then select an imputation method: do you want to replace the missing data or leave the missing data out?
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You can transform data in order to meet the assumptions. You can modify data to meet the assumptions, but also the improve the relationship among variables. What are dummy variables?
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