Ch 8 Sallis

Question 1

What is coding errors?

Answer 1

illogical values in the dataset

Question 2

How is coding errors and outliers detected?

Answer 2

By going through the data. Graphical representations like frequency distributions and various descriptive statistics are helpful in finding them

Question 3

Does outliers influence the analysis?

Answer 3

Outliers do not influence the analysis; they are simply part of the data presentation.

If you have a large dataset and the number of outliers is relatively small, you should probably consider removing the outliers. Common sense and argumentation play an important role.

Question 4

When does missing values occur?

Answer 4

Missing values occur when there is no observation recorded in one or more cells in a dataset. They leave holes that have not been assigned a number value. When data comes from questionnaires, missing values are simply due to the respondent not answering all the questions or an error in data input.

Question 5

What are three basic possibilities to deal with missing values?

Answer 5

1. Listwise deletion, which is to omit all cases with missing values. This works well when there is very little missing data relative to the sample size.
2. Pairwise deletion retains more data than listwise by using cases where data is available for each pair of variables in the analysis. Where listwise removes all cases with any missing data, pairwise only removes cases when the data is missing between each pair of variables being analyzed.
3. Impute (replace) missing values with a neutral value. There are several types of imputation. For example, the neutral value may be the average of the non-missing observations in the variable or it may be based on a pattern present in the data.