CONDICIONES DE APLICACIÓN DE LAS TARIFAS

Despite the process of data preparation and screening being quite time consuming, it is essential prior to the data analysis (Hair et al., 2006). The process is important for two reasons. Firstly, certain assumptions of the data are required in the estimation procedures for SEM, particularly about the distributional characteristics. Secondly, model fitting programs could fail to produce a solution if any data related problems occur (Kline, 1998).

Theobjective data screening process or examination is to discover any overlooked hidden effects due to problems such as normality issues, outliers or missing data. These issues are quite common with survey data collection. Hence, prior to the data analysis, these issues must be given priority and addressed accordingly.

5.6.1 Data preparation

In this study, data collection occurred using the Qualtrics.com platform. In total, 706 survey questionnaires were received as presented in Table 5.11. In sorting the usable survey questionnaires, seventeen survey questionnaires of Yoghurt with Live Cultures and nineteen survey questionnaires of Cholesterol Lowering Margarine were discarded. This is due to the problem of incomplete answers, as presented in Table 5.12.

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As mentioned earlier, three quality checks were utilised by Qualtrics. First, 'Force Response' settings were utilised for all multiple-choice type questions. This helped prevent respondents from ‘skipping through’ the questionnaire and leaving large portions of the dataset to blank. The second quality check applied was ‘attention filters’. They are used to help reduce the number of ‘straight-liners’ and ‘speeders’ for an online survey. Basically, these attention filters questions can be used to verify whether respondents are 1) reading the questions carefully and 2) following instructions. Two attention filters were added in both questionnaires in this survey to ensure that respondents fully read and understood each of the questions. Those respondents who did not fully read and follow the instructions of attention filters were screened out from the survey and not being counted as valid respondents. The third quality check used to focus on ‘survey duration’. As advised by Qualtrics’, in order to control the minimum time, it takes respondents to submit the questionnaire, the industry standard is applied. Using the average duration recorded during the soft launch as a reference, the industry standard is to set a minimum period of one-third of the time. Any attempt to answer below this benchmark time, was not accepted for thecount towards the project total. In relation to this study, prior to the setting appropriate survey duration, a soft launch of the survey took place involving 30 respondents for each questionnaire. Based on the average time of a soft launch phase, the appropriate minimum time setting applies. The new minimum time setting applies to the full launch survey. For this reason, any respondent who answered in less than 3 minutes were screened out from the survey. This was designed to ensure the

respondents allowed reasonable and proper time to answer all questions.

As detailed in Table 5.12, despite the total predetermine number of respondents of each functional food product has been set as 350 prior to the process of data collection, the total number of usable survey questionnaires (for both products) collected by the Qualtrics was 742 (372 for Yoghurt and 370 for Margarine). According to the Qualtrics system administrator, such extra data collection is a normal practice as to ensure the usable data is sufficient. However, these 742 responses were subjected to data screening prior to

130 Table 5-11 The Number of Questionnaires Received

Research subject Data Collection Method Number of questionnaires received

Functional food I (Yoghurt with Live Cultures)

Web-based questionnaire (Qualtrics panel)

372 Functional food II (Cholesterol

Lowering Margarine)

Web-based questionnaire (Qualtrics panel)

370

TOTAL 706

Table 5-12 Number of Usable Survey Questionnaires

Description

Subject Functional food I

(Yoghurt with Live Cultures) Functional food II (Cholesterol Lowering Margarine) Survey received 372 370 (-) Incomplete questionnaires 17 19

Net number (raw data) 355 351

(-) Standard deviation value below 0.5 10 6

Net number usable data 345 345

5.6.2 Data screening

In the screening process, the collected data were coded, and analysed using IBM SPSS Windows 22.0 (SPSS, 2013). In order to identify possible problems such as data entry or coding errors and whether the data was normally distributed, the statistical analysis utilised FREQUENCIES. The calculations involved an analysis of means, standard deviations,

skewness and kurtosis.

Both datasets (Yoghurt with Live Cultures and Cholesterol Lowering Margarine) were subjected to data screening. The process consisted of 3 steps. The first step was to find missing data in rows. However, after the screening, there were no missing data found in rows for all data. The second step was to find unengaged responses. At this stage, all completed questionnaires with a standard deviation of 0.5 and below were discarded. The latter suggests the respondent answered questions by giving the same value for all. Such responses should be eliminated as the respondent simply answered questions mechanically. For this reason, 10 respondents were removed from the Yoghurt with Live Cultures dataset and 6 respondents were removed from the Cholesterol Lowering Margarine dataset. Table 5.12 presents this information.

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5.6.3 Outliers

According to Byrne (2000), outliers refer to cases which produce a substantial

different score/marks than the overall set of data. Furthermore, High (2013) indicated, among possible reasons, outliers include rare events and data entry errors. The identification of outliers may involve multivariate tests, visual aids, and univariate tests (High, 2013). In particular, box plots, stem and leaf plots, and graphical evaluation of the QQ plots (Quantile- Quantile Plot) provide ways of identifying possible outliers.

The assessment of potential outliers utilised an inspection of boxplots. Precisely, the 1.5 x IQR (Interquartile Ranges) rule was used to define an outlier. It can be described by firstly, anything below Q1-1.5 IQR or secondly, above Q3+1. 5 IQR.

In the search of a possibility of evidence of outliers in the present study, boxplots were produced to inspect all the variables. No significant issues were identified, probably stemming from the fact that all constructs are assessed using a 7-point Likert scales.

5.6.4 Normality

Structural Equation Modelling (SEM) requires normality in the data. In brief, normality produces a normal distribution shape of data of respondents (Hair et al., 2010). According to DeCarlo (1997), univariate normality is established when a mean = 0, standard deviation = 1 and a symmetric bell-shaped curve. Meanwhile, the relevant tests for normality are Skewness and Kurtosis. The guideline of a normal distribution is based on the

requirement of Skewness and Kurtosis values within a range of ±2 (Gravetter and Wallnau, 2014). The data collected in this study satisfies the guideline criteria. The detail of the result of this assessment is presented in Chapter 6.