NORMAS DE CONVIVENCIA

Having constructed the survey questionnaire, the next stage is validation of this survey instrument as discussed earlier in chapter 3. This will take the form of a pilot study that will occur in two phases. The essence of piloting a survey is to increase the reliability, validity and usability of the survey (Cargan, 2007). As a result, before administering this questionnaire within the selected sample, a number of validation techniques will be used. The first validation process will test the content validity. Content validity is the extent to which a research instrument accurately measures all aspects of a construct (Heale and Twycross, 2015). It also answers the question that to what extent the selected sample in an instrument or instrument items is a comprehensive sample of the content (Zamanzadeh et al, 2015). Zamanzadeh et al (2015) suggested that it provide information on the representativeness and clarity of items. Also, help improve an instrument through achieving recommendations from an expert panel i.e., the content validity of instrument can be determined using the viewpoints of the panel of experts by considering the importance of individual items within an instrument. However, it should be noted that it also has some drawbacks such as the subjective nature of the experts’ judgement, which might lead to bias (Heale and Twycross, 2015).

In recent time, diverse methods for quantifying expert’s degree of agreement or content validity ratio regarding content validity of an instrument have been proposed. This includes methods like averaging experts’ ratings of item relevance and using a pre-established criterion of acceptability, using coefficient alpha to quantify agreement of item relevance by three or more experts, computing a multi crater kappa coefficient etc (Polit et al, 2008). However, one approach recommended years ago has special relevance in the field of research i.e. the approach proposed by Lawshe (1975) which is commonly used in the studies that involves more than 5 experts. This approach is called the Content Validity Index (CVI) which involves having a team of experts indicating whether each item on a scale is consistent with or relevant to the construct, computing the percentage of items deemed to be relevant for each expert, and then taking an average of the percentages across experts (Polit et al, 2008). These experts are expected to rate items into one of three categories using a scoring process of 1 to 3:

• Essential

• Useful, but not essential • Not necessary

And then, the items deemed “essential” by a critical number of panel members are then included within the final instrument, with items failing to achieve this critical level discarded. In all, to quantify the CVR for each item based on the ratings of the experts, a content validity index needs to be estimated. Lawshe (1975) derived a formula for estimating the CVR for individual items, which is provided below:

𝐶𝑉𝑅 = 𝑛𝑒− 𝑁 2 𝑁

2

Where n_{e is the number of panellists indicating essential and N is the total number of panellists.} Given that the content validity of this study involved 15 experts, CVI method was identified as suitable and therefore was used in this study.

4.3.1 The content validity exercise for this study

As earlier mentioned that 15 expert panels will be utilised, the content validation for this study will involve pre-testing the questionnaire among the panellist. These experts were identified using a self-selection process. These experts consisted of 7 academic practitioners and 8 industrial specialists of which some of them are age 50 and above. These experts were selected from different disciplines so as to enable this study to receive concrete suggestions and limit the bias that might result from individual expert’s ideas. Table 4.2 provides information on the experts used for the study.

Table 4. 2; Content Validation – Experts Panel

Category Participant Area of expertise Academic practitioners A Computer science B Engineering C Environmental management D Statistics C Information systems E Engineering F Mechanical engineering Industrial specialists G Information systems H Research analyst I Information technology J Accounting K Accounting L Engineering M Data analyst N Data analyst

Furthermore, the role of the experts was to provide the length of time it took to complete the questionnaire as well as provide some feedbacks on the questionnaire developed. As mentioned earlier, for the validation aspect, the experts were required to rate each item based on its relevance. In terms of the total time it took to complete the questionnaire, this was calculated by averaging the total time it took each expert to complete the questionnaire. As a result, the average time it will take a participant to complete the questionnaire is approximately 12.20 minutes. A sample of the questionnaire providing information required from the experts for the content validity process is provided in appendix. Equally, the experts were required to provide feedbacks with regards to the problems identified in the questionnaire as well as suggestions that will help in improving the questionnaire. These feedbacks are useful for developing a more understandable and clearer questionnaire. With this in mind, the questionnaire was modified before disseminating it for the pilot study. Table 4.3 provides details of the total completion time of each expert.

Table 4. 3: Calculating the average completion time of the questionnaire

Dr E: 10 minutes Miss L: 11 minutes Dr S :11minutes Dr O :11minutes Dr N: 12 minutes Mr S :12 minutes Mr Z :14 minutes Dr C :11 minutes Dr A :12 minutes Miss O: 18

minutes

Mr I :10 minutes Mrs N: 12 minutes Barr N: 11minutes Mr Y: 14 minutes Dr O: 14 minutes

Average completion time: 12.20 minutes

Furthermore, as stated earlier, the experts were required to validate the content of the questionnaire based on the relevance of each item. This aspect of the content validity exercise was aimed at examining the viability of the research as well as evaluating how suitable the survey is to the target population. This would be done using the rating criteria that was provided earlier as stated by Lawshe (1975) which will in turn help this study to calculate the CVR. Once replies were received, CVRs were calculated using Microsoft Excel. All the measurements met the accepted CVR value of 0.60.

Further, to quantify the CVR for each item based on the ratings of the experts, a content validity index needs to be estimated which is done by averaging the CVR of the accepted items and the threshold is 0.80 (Prion et al, 2016). Prion et al (2016) suggested that it is more efficient to report the overall content validity index score than each induvial item CVR. Therefore, the CVI value for the overall items is 0.83, which is an acceptable value. Table 4.4 shows the result of the CVR calculation.

Table 4.4: Calculation of the content validity ratio for each individual item

Questions Essential

Useful but not necessary

Not

Necessary CVR Values Accept/Reject

1 14 1 0 0.87 Accept 2 12 3 0 0.60 Accept 3 13 1 1 0.73 Accept 4 13 2 0 0.73 Accept 5 15 0 0 1.00 Accept 6 14 0 1 0.87 Accept 7 15 0 0 1.00 Accept 8 14 1 0 0.87 Accept 9 14 1 0 0.87 Accept 10 14 0 1 0.87 Accept 11 12 1 2 0.60 Accept 12 14 1 0 0.87 Accept 13 14 1 0 0.87 Accept 14 15 0 0 1.00 Accept 15 12 0 3 0.60 Accept 16 13 2 0 0.73 Accept 17 13 1 1 0.73 Accept 18 15 0 0 1.00 Accept 19 13 1 1 0.73 Accept 20 13 2 0 0.73 Accept 21 13 2 0 0.73 Accept 22 13 2 0 0.73 Accept 23 12 1 2 0.60 Accept 24 13 2 0 0.73 Accept 25 14 1 0 0.87 Accept 26 15 0 0 1.00 Accept 27 15 0 0 1.00 Accept 28 13 2 0 0.73 Accept 29 14 1 0 0.87 Accept 30 14 1 0 0.87 Accept 31 14 1 0 0.87 Accept 32 15 0 0 1.00 Accept 33 15 0 0 1.00 Accept