Question

Circle the letter of the best answer.

1. Many researchers tend to assume that any missing data that occur within their dataset:

1. Are random in nature.
2. Are caused by research participants not giving honest answers.
3. Reflect responses to a taboo topic where participants were ashamed to answer the question(s).
4. None of the above is correct.

2. There are three fundamental causes for outliers:

1. Data-entry errors were made by the researcher.
2. The participant is not a member of the population for which the sample is intended.
3. The participant is simply different from the remainder of the sample.
4. All three are correct.

3. There are three general assumptions involved in multivariate statistical testing:

1. Normality.
2. Linearity.
3. Homoscedasticity.
4. All three are correct.

4. If the researcher determines that the data have deviated from normal, she or he can consider transforming the data by:

1. A square root transformation.
2. A log transformation.
3. An inverse transformation.
4. All three are correct.

5. Linearity presupposes that:

1. There is a straight-line relationship between two variables.
2. The variability in scores for one continuous variable is roughly the same at all values. of another continuous variable.
3. Both (a) and (b) are correct.
4. Neither (a) nor (b) is correct.

Answer 1

Q.1 Many researchers tends to assume that any missing data that occur within their dataset

(a) Are random in nature.

This is unrealistic strong assumption in practice.

Q.2 There are three fundamental causes causes for outliers

(d) All three are correct

Reason:

An outlier is a data point that is distant from other similar points. An outlier may be due to variability in the measurement.There may be due to wrong data entry. Outliers arise due to changes in system behaviour, human error, instrument error or simply through natural deviations in populations. A sample may be from outside the population being examined.

Q.3There are three general assumptions involved in multivariate statistical testing

(d) All three are correct

Reason:

There are three general assumptions involved in multivariate statistical testing: Normality, Linearity, Homoscedasticity. Data that do not conform to these assumptions particularly for inferential testing, the results of the analysis may be biased, so these three assumptions are to be fulfilled.

Q.4 If the researcher determines that the data have deviated from normal, she or he can consider transforming the data by:

(d) all three are correct

Reason:

Any of the transformation can be made according to the nature of the data to make it normal.

Q.5 linearity presupposes that

(c) Both (a) and (b) are correct

Reason:

By linear we mean there is a straight line relationship between two variables. If we vary one variable then the other variable also changes linearly.