Sick and Tired: Understanding and Managing Sleep Difficulties in ...

2001; Revicki, 2002). Tabachnick and Fiddell (2007) suggested that a small number of
randomly missing values are less of a concern and that different ways of dealing with
missing data leads to similar results. This perspective is supported by Hawthorne and
Elliot (2005) who revealed in a study exploring the comparison of different methods of
dealing with missing data, that person mean substitution is the „method of choice‟ when
at least 50% of data are available for the remainder of a scale. In addition, deleting cases
with any missing values would have significantly reduced the sample size for this study
and may have biased the sample (De Vaus, 2002). Therefore as the data was considered
missing at random and less than 10% of the values were missing, person mean
substitution was conducted based on the other items available for each case on the given
scale before the analysis (Hawthorne & Elliott, 2005; Scheffer, 2002). The drawback of
the person mean substitution method is that this approach can reduce the correlations
between variables as the variance is reduced (Scheffer, 2002; Tabachnick & Fidell,
2007). Therefore to check the effect of this procedure for dealing with missing data,
data analysis was completed before (excluding the cases with missing values) and after
the mean substitution as suggested by Tabachnick and Fiddell (2007). The procedure
had little effect on the results.
The questionnaire data was scored according to the individual questionnaire instructions
and the resulting variables were checked for skewness and kurtosis using the explore
function within SPSS 16.0. Skewness and kurtosis scores of below 0.3 were used to
indicate if the data in each variable was normally distributed (Field, 2009). All
subscales of the RAND SF-36, the COPE Scale, the PSQI and the negative affect scale
of the PANAS revealed skewed distributions; the medians were reported as the measure
of central tendency. Mean scores were calculated for the age, length of illness and the
PSQI Global score variables which met the assumptions of ratio level data and revealed
a normal distribution.
Of the 101 participants included in the analysis, 94 were female (93%) and 7 were male
(7%). This translates into a female to male ratio of 14:1, which is slightly higher than
reflected in pervious prevalence studies. Thirty five participants (34.7%) were employed
at the time of completing the questionnaire and of these; three participants (3%) were
required to work shifts, which is known to affect sleeping patterns. Excluding the data
from these three participants did not greatly alter the overall pattern of results and
therefore their data was included in the analysis. At the time of completing the
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