Data analysis

Questionnaire scores and other descriptive data were logged onto an SPSS (version 23) spreadsheet data file by the researcher after the final visit.

This section sets out the methods used for data cleaning for further data analysis. Once the research data was collected, the process of preparing it for analysis began. This part describes the process of scanning returned questionnaires, cleaning the data, checking for bias in the analysis, missing values and data checks.

The first issues concerned the accuracy with which the data was entered into the data file and also the consideration of factors that could have produced distorted correlations. In doing so, the original file was visually checked, and also because it is a large data file, screening for accuracy involved an examination of descriptive statistics, and for graphic representation of the variables, SPSS (version 23) was used to examine univariate descriptive statistics.

Secondly, any missing data was assessed and dealt with. It is essential to understand the pattern of missing data. The reason for this is that non-randomly missing values are serious, no matter how few of them there are, they will affect the generalisability of the results. The SPSS Missing Values Analysis (MVA) was used, which is specifically designed to highlight patterns of missing values as well as to replace them in the data set. A t- test was requested to see if the matter was related to any of the other variables, with α= .05 and tests were carried out only for variables with at least 5% of data missing. The EM syntax requests a table of correlations and a test for whether data are Missing Completely At Random (MCAR). Thus, Little’s MCAR testwasperformedto find out whether the missing data was random or non-random. The output showed that for the Little’s MCAR test: Chi-square= 1774.729, DF=1726, Sig=.202. Thus, the decision was made to perform techniques to replace missing data for randomness.

It is important to consider the level of measurement to determine which statistics, graphs and analysis it is possible to use. Some of the variables in this study are nominal or categorical, for example sex. The nominal data is one that has two or more categories, but there is no intrinsic ordering of the categories. Thus, numbers do not imply order. To summarise the data, the frequency/percentages have been used. However, the data is

ordinal. An example of ordinal data is Positive Psychological Change (PPC) upon the Silver Lining Quesitonnaire-38 (Sodergren, 2000, 2002). These variables are in order, but the interval between variables may not be equal. Likert scales are ordinal data; ordinal data can provide frequencies (percentages). Some psychology researchers believe that the mean should never be calculated for ordinal data. However, it is quite common, particularly in the social sciences and in psychology, to consider people’s behaviour and to report the mean values of ordinal data (Nunnally & Bernstein, 1994). To summarise ordinal data the most common summary measures are: frequencies, proportions and sometimes mean. The testing for interval ratio data are: test for a mean; the difference between two means (independent samples) and regression analysis. Therefore, the level of measurement is justifiable, although it is ordinal it could be treated as interval. Psychologists are flexible in terms of the level of measurement, so when the level of measurement is interval, it is also possible to show the result as mean and SD, and it is possible for the data to be shown on a scatter plot (Nunnally & Bernstein, 1994).

In order to determine whether the data has met the assumption for parametric analysis, the data was visualised; the level of measurement of the variables was considered; the Kolmogorov-Smirnov and the Shapiro-Wilk test were employed, and the shape of frequency histograms for the variables was checked.

Finally, a questionnaire was scored and other descriptive data logged onto an SPSS (version 23) spreadsheet data file by the researcher after the final visit. The findings from the study are based upon the information gathered as a result of the methodology that was applied. 3.5. Aim one: The likelihood and extent of Positive Psychological Change

To establish the first aim of the thesis, which is to describe the likelihood and extent of PPC in people with RA, the following procedures were conducted:

• The normality of the data upon the SLQ-38 scale was evaluated.

• The SLQ-38 mean and SD was reported.

• The SD of the scores, which is an estimation of the average variability of a set of data measured in the same units of measurement as the original data, was demonstrated.

• The overall SLQ-38 scores were obtained. The extent to which the participants agree or disagree with the 38 statements using a five-point Likert scale: (1) strongly

disagree; (2) disagree; (3) not sure; (4) agree and (5) strongly agree state that an overall score was obtained by giving a value of ’ 1’ to responses of strongly disagree and ‘5’ to strongly agree. Therefore, an overall score reflects the total number of items with which the individual agrees. For the purpose of the current study, the continuous scoring was adopted to avoid an unnecessary amount of information throughout the analysis (Breakwell et al., 2006).

The question of the likelihood of PPC and its extent is the subject of this section. This part explores: what is the proportion of PPC in individuals with RA in their scores on the SLQ- 38?

The study hypothesis was: PPC occurs across individuals with RA using the SLQ-38 scores. Data obtained were tested for the normality as explained in statistical analysis in this section. The histogram demonstrated the SLQ-38 scores. The data appears to be normally distributed and seems to show roughly symmetrical distributions, with equivalent means of 95.09 across items, which demonstrates the centre of the distribution of scores upon the SLQ-38 scores. The SD was 26.09, which represents an estimation of the average variability of a set of data measured in the same units of measurement as the original data.

This data has been analysed based on SLQ-38, with the mean score 95.09∓ 26.09 SD. The Cronbach’s alpha, across the present study samples, was .95, which is slightly higher than Sodergren (2000), proposing a high level of homogeneity amongst the SLQ items. The Cronbach’s alpha for the overall reliability of the scale was used to determine the extent to which the items on a scale were measuring the same underlying dimension. This is slightly higher at 0.90; P<0.001 (Sodergren et al., 2002), showing good retest reliability among the SLQ-38 items. For the thesis data file, all data have item-total correlations above 0.3 and this is well within the region specified by Kline (2000). Therefore, the SLQ-38 appears to have good internal consistency, α= 0.95 and all items appear to be correlated to the total scale to a good degree, suggesting a high level of homogeneity among the SLQ-38 items for this study population. Field (2009) suggests that Cronbach’s alpha of around 0.8 is good for a questionnaire. Indeed, if it is supposed that PPC is identified by the scores beyond the one

SD, 121, on SLQ-38 mean, then approximately 34% of the people reported PPC (Graph 3.1, See SLQ).