CBMR Pre-Quiz Nr. 6

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Unsatisfactory responses are commonly handled by returning to the field to get better data, assigning missing values and discarding unsatisfactory participants.

The coding of unstructured or open-ended questions is termed grouping.

The following options are available for the treatment of missing values: (1) substitute a neutral value, (2) substitute an imputed response, (3) casewise deletion, and (4) pairwise deletion.

Dummy variables are values for missing information, usually -66, -77 or -99.

Statistical techniques suitable for analyzing data when there are more than two measurements on each element and the variables are analyzed simultaneously, are called multi-variate analysis techniques.

For a symmetric distribution, mean and standard deviation show the same value.

When the researcher wants to analyze a specific sub-sample in SPSS, he/she can use the SPSS option “SELECT CASES”.

The null hypothesis is a statement in which any difference or effect is expected.

The t test is an univariate hypothesis test using the t distribution, which is used when the standard deviation is unknown and the sample size is small.

If the significance level of the Levene’s test is below 0.05, the independent samples t-test is interpretable.

The expected value for any cell of a contingency table can be calculated as row sum * column sum / total sum.

Eta² measures the significance of the effect of X on Y and varies between 0 and 1.

The correlation coefficient is an index used to determine whether a linear or straight line relationship exists between X and Y.

Correlation analysis allows predicting values of the dependent variable.

R² measures the proportion of the total variation in Y that is accounted for by the variation in X.

The most frequently used procedure for fitting a straight line to a scattergram is the least squares procedure.

In marketing research, most relationships are deterministic.

An exploratory factor analysis (EFA) can be used in market segmentation for identifying homogenous groups of customers.

The eigenvalue represents the total variance explained by each factor.

If the number of variables is less than 20, the Kaiser criterion will result in a comparatively high number of factors.