Recoding

Survey items can be worded with a positive or negative direction:

·         Positively worded: e.g., I know that I am welcomed at my child’s school, I feel that I am good at my job, Having a wheelchair helps, etc…

·         Negatively worded: e.g., I feel isolated at my child’s school, I am not good at my job, having a wheelchair is a hindrance, etc…

·         Likert scaled responses can vary: e.g., 1 = never, 2= sometimes, 3 = always; OR

1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree

·         When creating a composite score from specific survey items, we want to make sure we are looking at the responses in the same manner.  If we have survey items that are not all worded in the same direction, we need to re-code the responses. E.g.: I want to make a composite score called “helpfulness” from the following survey items :  

o   5-point Likert scaled, where 5 = always   4 = almost always  3 = sometimes, 2 = almost never  1 = never

1.      I like to tutor at school   

2.      I am usually asked by my friends to help with homework

3.      I typically do homework in a group setting

4.      I do not go over my homework with others

In this example, survey items 1 – 3 are all positively worded, but survey item 4 is not.  When creating the composite score, we wish to make sure that we are examining the coded responses the same way.  In this case, we’d have to re-code the responses to survey item 4 to make sure that all responses for the score “helpfulness” are correctly interpreted; the recoded responses for survey item 4 are: 1 = always, 2 = almost always, 3 = sometimes, 4 = almost never, 5 = never.

Now, all responses that are scored have the same direction and thus, can be interpreted correctly: positive responses for “helpfulness” have higher values and negative responses for “helpfulness” have lower values.

·         Also, you may wish to change the number of responses.  For example, you may wish to dichotomize or trichotomize the responses.  In the example above, you can trichotomize the responses by recoding responses “always” and “almost always” to 3 = high, “sometimes” to 2 = sometimes, and “almost never” and “never” to 1 = low.  However, please be advised to make sure that you have sound reason to alter the number of responses.

Screening of the Data

Careful analysis of data applicability after collection and before analysis is probably the most time-consuming part of data analysis (Tabachnick & Fidell, 2001). This step is, however, of utmost importance as it provides the foundation for any subsequent analysis and decision-making which rests on the accuracy of the data. Incorrect analysis of the data during purification, including EFA, and before conducting confirmatory SEM analysis may result in poor fitting models or, worse, models that are inadmissible.

Data screening is important when employing covariance-based techniques such as structural equation modelling where assumptions are stricter than for the standard t-test. Many of the parametric statistical tests (based on probability distribution theory) involved in this study assume that: (a) normally distributed data – the data are from a normally distributed population, (b) homogeneity of variance – the variances in correlational designs should be the same for each level of each variable, (c) interval data – data where the distance between any two points is the same and is assumed in this study for Likert data, and (d) independence – the data from each respondent has no effect on any other respondent’s scores.

Many of the common estimation methods in SEM (such as maximum-likelihood estimation) assume: (a) “all univariate distributions are normal, (b) joint distribution of any pair of the variables is bivariate normal, and (c) all bivariate scatterplots are linear and homoscedastic” (Kline, 2005, p. 49). Unfortunately, SPSS does not offer an assessment of multivariate normality but Field (2005) and others (Kline, 2005; Tabachnick & Fidell, 2001) recommend first assessing univariate normality. The data were checked for plausible ranges and examination was satisfactory. There were no data out of range.