A quick introduction to the analysis of questionnaire data John Richardson

1. A quick introduction to the analysis of questionnaire data John Richardson

2. Measurement scales nominal categorisation = ordinal rank ordering >, =, < interval equal intervals >, = <, +, – ratio absolute zero >, = <, +, –, , 

3. Frequency distributions A frequency distribution shows all the possible scores in a distribution and how often each score was obtained. A bar graph shows the frequency distribution of a set of scores where the scores are arranged on the x axis and their frequencies are shown on the y axis. A histogram shows the frequency of a set of scores measured on an interval or ratio scale. The bars correspond to successive intervals on the scale. (A histogram is a bar graph in which the bars are touching each other.)

4. Measures of central tendency • The (arithmetic) mean: the sum of the scores in a distribution divided by the number of scores (X). • The median: the point on the scale below which 50% of the scores in a distribution fall. • If the number of scores is odd, the median is the middle score when the scores are ranked. • If the number of scores is even, the median is the average of the two middle scores when the scores are ranked. • The mode: the most frequent score in a distribution.

5. Measures of central tendency, ctd. • The mean assumes an interval or ratio scale. • The median assumes an ordinal, interval or ratio scale. • The mode assumes a nominal, ordinal, interval or ratio scale.

6. Measures of variability The range is the difference between the highest and lowest scores in a distribution. A deviation score is the difference between the original score and the mean of the entire distribution. The variance of a set of scores is the average squared deviation score. The standard deviation of a set of scores is the square root of the variance.

7. Correlation A linear relationship between two variables is one that can be most accurately represented by a straight line. A perfect relationship is one in which all of the points fall on the line. An imperfect relationship is one where a relationship exists but all of the points do not fall on the line. A positive relationship exists when there is a direct relationship between the two variables. A negative relationship exists when there is an inverse relationship between the two variables.

8. Correlation, ctd. A correlation coefficient expresses quantitatively the magnitude and direction of a relationship: +1 a perfect positive relationship  an imperfect positive relationship  0: no relationship  an imperfect negative relationship  –1 a perfect negative relationship

9. Correlation, ctd. The linear correlation coefficient Pearson r is a measure of the extent to which pairs of scores occupy the same (or opposite) positions within their respective distributions. The square of Pearson r quantifies the proportion of the total variability in one of the variables that is accounted for by the other variable. [If r = 0.80, r² = 0.64, so y explains 64% of the variability in x.]

10. Correlation, ctd. Pearson r assumes that the data are measured on an interval or ratio scale. For ordinal scales, use the Spearman rank order correlation coefficient rho (rs). For nominal scales, use the phi (φ) coefficient. Finally, note that correlation does not imply causation.

11. Reliability A research instrument is reliable if it yields consistent results when used repeatedly under the same conditions with the same participants (that is, it is relatively unaffected by errors of measurement). It can be measured by various coefficients of reliability, all of which vary between zero (reflecting total unreliability) and one (reflecting perfect reliability). (In practice, instruments of poor reliability may actually yield estimates that are less than zero.)

12. Reliability, ctd. Test-retest reliability is obtained by calculating the correlation coefficients between the scores obtained by the same individuals on successive administrations of the same instrument. If the interval is too short, the participants will become familiar with the instrument and may even recall the responses that they gave at the first administration. If the interval is too long, there may be genuine changes in the personal qualities being measured. In any case, longitudinal studies are hard to carry out because of drop-out between the two administrations.

13. Reliability, ctd. An alternative approach is to estimate an instrument’s reliability by examining the consistency among the scores obtained on its constituent parts at a single administration. One such measure is split-half reliability: the items are divided into two subsets, and a correlation coefficient is calculated between the scores obtained on the two halves.

14. Reliability, ctd. The most common measure of reliability is Cronbach’s coefficient alpha. This estimates the internal consistency of an instrument by comparing the variance of the total scores with the variance on the scores on the individual items. (It is formally equivalent to the average value of split-half reliability across all the possible ways of dividing the items into two distinct subsets.)

15. Factor analysis Factor analysis is a technique for identifying a small number of underlying dimensions from a large number of variables measured on the same participants. Principal component analysis assigns the variance associated with the original variables to the same number of independent dimensions or components. It is based on the original correlation matrix among the variables. However, whereas the diagonal elements of this matrix have a value of 1.00 by definition, the off-diagonal elements are reduced by test-retest unreliability.

16. Factor analysis, ctd. The various forms of common factor analysis are only concerned with the variance that is common to two or more of the variables. They use an amended correlation matrix in which the diagonal elements are replaced by estimates of the communality of the corresponding variables, and so they acknowledge that the other elements in the matrix are reduced by test-retest unreliability. The most commonly used form of common factor analysis is called principal axis factoring in SPSS.

17. Factor analysis, ctd. The next problem is to determine the number of factors or component to be extracted. The eigenvalues express the proportion of variance accounted for by each factor. One commonly used rule of thumb is that of extracting the number of factors whose eigenvalues are greater than one in a principal component analysis. This is often inaccurate when tested on artificially generated data. With large numbers of variables, the eigenvalues-one rule tends to overestimate the true number of factors.

18. Factor analysis, ctd. An alternative procedure works by extracting factors up to the point where the difference between the successive eigenvalues reflects a relatively constant increment attributable to random error. This rule is known as the scree test, and it is more accurate than the eigenvalues-one rule when used with artificially generated sample data. In general, at least two different criteria should be used to justify extracting a particular number of factors.

19. Factor analysis, ctd. The extracted factors are then usually rotated to yield a more interpretable solution. Rotation tries to maximise the number of variables that show high or low correlations (or loadings) with each factor and to minimise the number of variables with moderate loadings. Orthogonal rotation results in factors that are independent of one another. This may make them easier to interpret.

20. Factor analysis, ctd. Oblique rotation results in factors that may be correlated with one another. This may be more plausible if the various dimensions result from overlapping sets of mental processes. If a factor analysis results in a number of oblique factors, then one can calculate the participants’ scores on those factors and subject them to a further (second-order) factor analysis.

21. A quick introduction to the analysis of questionnaire data John Richardson