Data analysis

The data analysis is one of the significant parts of any research as it helps to investigate the collected data and, from this, draw conclusions (Creswell et al., 2007). According to Jorgensen (1989), data analysis starts with the, ‘…breaking up, separating, or disassembling of research materials into pieces, parts, elements, or units.’ Thereafter, the researcher sorts them and looks for types, sequences, or patterns, and even combines quantitative and qualitative data in seeking evidence to address the initial propositions of the study (Yin, 2003). The aim of this process is to assemble or reconstruct the data in a meaningful way (Jorgenson, 1989). As stated by (Hartley and Hocking, 1971), data analysis helps to generate theories that are grounded in empirical evidence. As mentioned, this study gathered qualitative data from semi-structured interviews and quantitative data from a questionnaire survey. This section firstly describes the analysis techniques used for the qualitative data and secondly, that for the quantitative data.

4.9.1 Qualitative data analysis

The concentration on text rather than on numbers is a key feature of qualitative analysis. The ‘text’ that qualitative researchers analyse has most commonly come from an interview transcription or notes from participant observation sessions; however, the text can also refer to pictures or other images that the researcher examines (Lacey and Luff, 2001).

There are different phases to qualitative data analysis that are shared by most approaches (Lacey and Luff, 2001)

1. Documentation of the data and the process of data collection. 2. Organisation/categorisation of the data into concepts.

3. Connection of the data to display how one concept may influence another.

4. Corroboration/legitimization, by evaluating alternative explanations, disconfirming evidence and searching for negative cases.

5. Representing the account (reporting the findings).

Before the data is analysed, the researcher will transcribe all interviews. The process of transcribing allows the researcher to become familiar with the data (Riessman, 1993). The researcher will create Microsoft Word files for the interviews and will protect them by setting

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a password. For further protection, all these files will be saved in the researcher’s portable computer to which only he has access.

The researcher will use the content analysis technique. The content analysis carried out for the interviews was used to ascertain a pattern of responses amongst the participants according to the predefined categories (Haron, 2013). The analysis of the interviews began with the intra-case analysis of each case and was followed by cross-case analysis for all organisations involved. The researcher will use the qualitative software NVIVO program for data coding, management, and analysis.

This study will implement a multiple case study design where the data is analysed case by case through objective analysis and later by cross-case analysis (Stake, 2013). Thus, interviews and documents will be analysed for each case. In following the case-by-case analysis, all codes will be used to conduct the cross-case analysis. Similar codes across all cases will be kept as well as those that are significantly different (Creswell et al., 2007). The information coding process is classified by Saunders et al. (2009) as either ‘deductive or ‘inductive’. These categories are closely associated with content analysis as well as thematic analysis, and one method is selected prior to starting the coding process (Bernard and Bernard, 2012; Braun et al., 2014). In using deductive coding, the codes may be pre-selected before beginning the coding process (Vaismoradi et al., 2013). Inductive coding, on the other hand, depends on the data to develop codes and is frequently related to ‘Grounded Theory’ (Bernard and Bernard, 2012). In qualitative studies that start with an inductive approach, data is frequently coded inductively (Vaismoradi et al., 2013). Therefore, an inductive approach will be used in the coding of the data as it corresponds with a qualitative study.

Each interview will be recorded in digital audio and will be later transcribed for analysis purposes. Care will be considered to make sure that all the recorded interviews are transcribed professionally without any bias. Each interview will be read and answers to questions from interviewees will be grouped for further analysis. Codes will be developed using the ‘open coding’ mechanism proposed by DeCuir-Gunby et al. (2012). Open coding is the process of labelling raw data under various headings so that further analysis may be conducted to organise, categorise, quantify and identify relationships within the codes. The codes will then be carefully examined through a content analysis as well as a thematic analysis. Data saturation observations, as well as codes/labels that directly relate to BIM maturity, benefits, and client roles, will be used to create a conceptual framework for BIM maturity- benefits relationship.

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4.9.2 Quantitative data analysis

Quantitative data analysis regularly deals with statistical data analysis techniques. Some of the most commonly used techniques are chi-square analysis, correlation analysis, factor analysis, and so forth (Amaratunga et al., 2002). A quantitative data analysis plan generally consists of raw data assessment; data entry and transfer; data processing; communicating findings; data interpretation; and completing data analysis (Jennifer Mason, 1994). Saunders et al. (2009) recommend that numerical data gathered from surveys can be analysed using ‘descriptive’ or ‘inferential’ statistics. Descriptive statistics are utilised to describe the central tendency of the data as well as to define the dispersion of the data from the central tendency (Hinton et al., 2014). The central tendency itself is an examination of the values that can offer a general impression of the data (Saunders et al., 2009). Inferential statistics (advanced analysis), on the other hand, look at the data beyond the central tendency and are utilised to examine relationships, differences and trends within the numerical data. Inferential statistics allow the data to be tested for the strength and significance of the relationships between the variables (Saunders et al., 2009).

The quantitative analysis of this study is aimed at validating the conceptual framework that will be created from the results of the qualitative analysis on the interview data and the literature. The conceptual framework is expected to be validated/modified/refined from the data gathered from the online questionnaire. Hence, descriptive and inferential statistics are proposed for interpreting the results from the online questionnaire. This is due to the following reasons:

1. Descriptive analysis will be used to summarise a given data set in terms of average, mean, median, and standard deviations, which can either be a representation of the entire population or a sample of it.

2. The inferential analysis will also be used to measure the strengths of association between two variables, which will be achieved through the correlation technique. For this study, the two variables are the BIM maturity competencies and BIM uses benefits. In addition, the level of measurement can influence the type of analysis used. There are four levels of measurement (Sapsford and Jupp, 2006):

 Nominal: is hardly a measurement in that it refers to quality more than quantity. A nominal level of measurement is simply a matter of distinguishing by name, e.g., 1 = male, 2 = female. Even though the numbers 1 and 2 are used, they do not denote quantity.

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 Ordinal: refers to the order in measurement. An ordinal scale indicates direction and provides nominal information. Low/medium/high, or faster/slower are examples of ordinal levels of measurement.

 Interval: Interval scales provide information about the order and indicate equal intervals. From the previous example, if the distance between 1 and 2 were the same as that between 7 and 8 on a 10-point rating scale, then this would indicate an interval scale.

 Ratio (scale): in addition to possessing the qualities of nominal, ordinal, and interval scales, a ratio scale has an absolute zero (a point where none of the quality being measured exists). Using a ratio scale permits comparisons, such as being twice as high, or one-half as much.

While the research aim to find the relationship between maturity competencies and BIM uses benefits which classified as ordinal scaled, therefore, Spearman's correlation coefficient and Kendal rank have the ability to measures the strength of association between two ranked variables, which represented in this study by maturity levels and benefits assurance levels. There are difficulties associated with using Spearman’s test with data from either very small samples which less than 7 or large samples which large 60, therefore spearman correlation will be used for all BIM uses except who has less than 7 responses M. (Saunders et al., 2009).

a) Kendall rank correlation is a nonparametric test that measures the strength of dependence between two variables. If we consider two samples, a and b, where each sample size is n, we know that the total number of pairings with a b is n(n-1)/2. The following formula is used to calculate the value of Kendall rank correlation:

Kendall rank correlation= 1𝑛𝑐−𝑛𝑑

2𝑛(𝑛−1)

Where:

Nc= number of concordant Nd= Number of discordant

b) Spearman correlation: Spearman rank correlation is a nonparametric test that is used to measure the degree of association between two variables. It was developed by Spearman; thus it is called the Spearman rank correlation. Spearman rank correlation test does not assume any assumptions about the

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distribution of the data and is the appropriate correlation analysis when the variables are measured on a scale that is at least ordinal. The formula used to calculate Spearman’s correlation coefficient is shown below:

Where:

di = Difference in paired ranks, n= Number of responders

checking if this ρ value is significant, a Spearman’s Rank significance table or graph must be used as shown in Figure 4.10.

Figure 4.10: The significance of the Spearman’s rank correlation coefficient and degree of freedom

The correlation analysis will be carried out on each BIM uses to find the relationship between BIM maturity competencies and BIM uses benefits.