DEFINICIÓN DE CALIDAD

The quantitative data generated in this study was analysed using the Statistical Package for the Social Sciences software (SPSS, version 22). Prior to exporting the survey data from the Bristol Online Survey platform, unique identification numbers were allocated to the responses. This enabled instances of missing data, errors and outlying scores to be checked prior to statistical analysis. An SPSS data file was prepared by creating abbreviated labels for each of the dependent and independent variables. The dependent variables in this study included attitudes to IPW, and attitudes to IPPL for students. Numeric codes were assigned to the survey’s Likert scale responses (1 = Strongly Disagree to 4 = Strongly Agree) and negatively orientated questions were reverse scored (1 = Strongly Agree to 4 = Strongly Disagree). The independent variables included: gender, area of work, years of experience, professional governing body, previous IPE experience, and type of IPE

71 experience. A record was maintained of all codes and variable labels used, to enable easier identification during analysis.

4.9.1 Descriptive statistics

Descriptive statistics were performed to summarise the characteristics of the sample and to provide a summary of the mean scores from the IPW and IPPL survey. These are reported in Chapter 6, Section 6.2. Although descriptive statistics are often viewed as a way of simply enumerating and organising data (Cohen et al., 2011), identifying the sample characteristics helped to determine the relevant independent variables, which were analysed with inferential statistics to test the hypotheses in this study. Evaluating the results of the descriptive statistics also enabled errors and missing data to be identified. These were then cross-checked with the original completed survey and with interview data, to determine if omissions in the survey responses were addressed in the interview. A numerical code of ‘99’ was attributed to the remaining instances of missing data. This assisted in the identification of patterns of commonly omitted items and ensured that the calculations of mean scores were accurate. Where there were instances of missing data, the “exclude cases pairwise” option in SPSS was used to ensure that the missing cases were excluded from the analysis (Pallant, 2013).

4.9.2 Inferential statistics

The use of mixed factorial analysis of variance (ANOVA) was identified as the most appropriate method of statistical analysis of the quantitative data in this study. As discussed in Chapter 2, Section 2.8, this study tested the following hypotheses:

• There are significant differences in the attitudes of practice mentors to IPW • There are significant differences in the attitudes of practice mentors to IPPL

for students

This statistical test enabled differences in attitudes to IPW and IPPL to be investigated between multiple groups of professions, and the measurement of the effect of multiple variables (governing body, gender, area of work, number of years’ experience as a professional, previous IPE experience, and type of experience) on attitudes to IPW, and IPPL for students. Significance levels were set at p < 0.05.

72 There has been much debate around the most appropriate statistical methods used with Likert scale data. The main point of contention relates to the classification of Likert scale data as ordinal or interval. According to Field (2009), Coolican (2009), and Pallant (2013), the assumptions which should be satisfied for the use of parametric tests are: data at interval level, normally distributed data, independence of measurements, and homogeneity of variance. However, Carifio and Perla (2008) and Norman (2010) argue that parametric statistics can be used even with data that is not normally distributed. Data generated from a Likert scale can be classified as interval data and can still be robustly analysed with parametric statistics.

Prior to examining the effect of the independent variables on attitudes to IPW, and IPPL for students, the mean scores and standard deviation were evaluated, to determine if attitudes were positive or negative. Outlying scores were identified and double checked in the original survey to rule out any errors. The influence of any outlying mean scores were taken into consideration by examining the 5% trimmed mean and comparing this value with the overall mean. According to Pallant (2013), outlying cases can be retained when the overall mean and trimmed mean are similar, due to the minimal influence that the outlying mean score will have on the overall mean.

Although statistical tests such as ANOVA are viewed as robust to violations of normality assumptions (Norman, 2010), it is viewed as good practice to evaluate the distribution of data, and to comment on departures from normality (Kim, 2013). According to Tabachnick and Fidell (2014), normal distribution is confirmed by skewness and kurtosis value of zero. Assessing significance of departures from normality can be determined by calculating the skewness and kurtosis z-scores (by dividing the skewness and kurtosis values by their respective standard deviations). Kim (2013) explains that departures from normality can be deemed significant if the z-score falls above or below the level of 1.96 for a sample size of less than 50, and 3.29 for a sample of more than 50 and less than 300. To evaluate the distribution of data and departures from normality in this study, histograms for each independent variable and sub-scale of the survey were analysed as a visual evaluation of the distribution of the data, and the skewness and kurtosis values and associated z- scores were analysed to determine the extent of any departures in normality. This is discussed further in the results chapter (Chapter 6).

73 In addressing the other assumptions to be met with the use of parametric tests, independence of measurements was facilitated by the data collection strategies. As described in earlier in this chapter (Section 4.5) study participants were sent the electronic link to the online survey so that they could complete the survey individually, as opposed to a group setting, where other participants may have influenced responses. In addition, as part of the inferential statistical analysis, homogeneity of variance was tested via Mauchly’s test of sphericity. The results of this test are discussed in Chapter 6.

4.10 Chapter summary

This chapter introduced the unit of analysis for this case study and has identified that that this study took place within one health board and local authority within Scotland. In specifically targeting health and social work professions who mentor students during their placements, a range of sampling strategies were used to recruit participants to this study. A number of challenges arose for the researcher in relation to recruiting a representative sample group. However, as highlighted in this chapter, gatekeepers were instrumental in gaining access to the study site and raising awareness of the study.

As a mixed-methods case study, it has been previously discussed that this study used quantitative and qualitative methods in sequence to generate and analyse data. This chapter focused on the quantitative methods, as the core method and first in the sequence. An online survey which was adapted from two pre-validated scales (previously used in interprofessional research) facilitated the measurement of practice mentors’ attitudes to IPW, and IPPL for students, and the analysis of which variables affect these attitudes. This chapter presented the results of the test-retest measures of reliability which confirmed that it was an appropriate tool to use for the purposes of this study. Chapter 5 focuses on the qualitative methods which were second in the sequence of methods employed by this study. As a follow-up contribution to the core quantitative methods, semi-structured interviews were used to explore practice mentors’ perspectives of the enablers of and barriers to IPW, and IPPL for students.

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