Greener (2008) defines research reliability as the degree to which research can be consistent over a time period; this implies that a study’s level of repeatability over a
179 time period would be an essential attribute of the research design because it offers clarity and transparency as to how the process was undertaken. Greener argues that once the reader is able to see the attribute of repeatability, they would develop confidence in the research because they can see that the results have not been manipulated (Greener, 2008). Fellows and Liu (2008) support this view by stating that, if the research is replicated, it needs to demonstrate that it could produce similar results regardless of who undertakes the repeated process. Once there is consistency in the findings, the research can be said to be highly reliable (Saunders et al., 2009). However, if there is an element of preferential bias from the researcher or if the design of the research is subjective, it could be difficult to achieve a high level of reliability; hence, the results could not be replicated (Saunders et al., 2009). The impact of poor reliability concerning research is that such studies may not be useful to the reader and could easily be discarded as flawed and therefore not generalisable to the population (Black, 2010; Fellows & Liu, 2008).
Cronbach’s Alpha is commonly used to test reliability. The alpha coefficient ranges between 0 and 1; α
<
0.5 indicates that data is not reliable while an alpha figure closer to 1 indicates a high level of consistency. Table 6-7 presents the value indicators for the alpha coefficient that the researcher used with SPSS to calculate values for Cronbach’s alpha.Table 6-7: Compiled From Blaikie (2003)
For this research, the process was deemed to be highly reliable because there was a deliberate decision to use both qualitative and quantitative methods for data collection, data analysis, and the critical examination of both sets of data. This was
180 suggested by Greener (2008), who stated that triangulation was one of the main strategies that a research design should adopt in order to produce reliable results.
6.15
Validity
Validity, according to Greener (2008), is the degree to which the results from a study can have face validity, construct validity and internal validity. In other words, the three angles for validity are concerned with the assurance that the findings of a study are a true reflection of what they really appear to represent (Saunders et al., 2009). Face validity refers to face-value validity from non-researchers who, according to Greener (2008), can express that the results of a study are valid based on the way the process has been designed. Face validity is vital to ordinary people and research participants because it can promote their participation in the research if they can believe that on face value the research design and process is valid. Construct validity, on the other hand, refers to the degree to which the research process can measure what it aimed to measure (Greener, 2008). The implication of construct validity for the research lies in the way decisions are made with regards to the design of the data collection tools as well as the data analysis process (Greener, 2008). If the decisions are justified and can be confirmed to be the case, then the research process could be said to be highly construct valid. Construct validity is fundamental to the design of questions (Fellows & Liu, 2008); if the wrong questions are asked, the measurement of the data could be invalid (Greener, 2008).
Internal validity tests causality between variables, meaning that there are times when the results could be linked to clear causality between variables, and situations where there can only be association between variables (Greener, 2008). According to Saunders et al. (2009), validity can be at risk if a researcher presumes that their work must tally with the historical findings in their field. Other factors include the way the testing of results is carried out as well as the instruments used in the testing process (Saunders et al., 2009). They also observed that if there are dropouts in terms of research respondents from a particular sample, the research result must be invalid, or the questions designed for collecting the research data were ambiguously designed (Saunders et al., 2009).
181 For this research, the design of the research process was made transparent, from the research philosophies used to the data analysis tools and designs employed. For this reason, key steps – such as the use of interpretive structural modelling (ISM) as a tool to model the key areas of the subject matter of knowledge as well as the use of a preliminary survey prior to the issuance of the final survey questions – have all been vital steps to assure the validity of the research process. The results from the research are therefore envisioned to be highly reliable and valid due to the transparent nature of the research process undertaken.
6.16
Research Process
The summary of the research process used in this project has been abstracted in Figure 6-6. Firstly, it shows that the philosophy of interpretivism and positivism were established in the design of the instruments for data collection; secondly, the preliminary data collection using interviews was critical to creating a base for the research. In this way, the application of the grounded theory was a possibility at this phase. Thirdly, after the interview data collection, it was possible to develop the questions that could be useful in developing a model for creating the strategy for talent management in Qatar using the construction industry as the vehicle for testing the theories. Fourthly, data triangulation was conducted after the questionnaire survey results were analysed, creating a basis for which conclusions and answers to research questions were to be tested.
182
Figure 6-6: Sequence For The Research Process
Start Data (Qualitative and Quantitative) Decision on Data Collection and Sampling? Thematic
Analysis Regression Analysis
End
Yes Yes
Research Philosophy
(Positivism and Interpretivism)
Research Approach (Deductive Reasoning) Research Strategy (Survey) Methodological Choice (Mixed Method) Yes Questionnaire Survey Yes Semi-structured Interviews
Decisions for time horizons?
Cross-sectional (Snap shot)
Decision for Data Analysis? Interview Template (Appendix B) Questionnaire Template
183
6.17
Summary
This research methodology explained and justified the methods used to collect the primary information required for this research. Positivism and interpretivism, as research philosophies, were used as both qualitative and quantitative methodologies in gathering and analysing the data. Deductive reasoning was also felt to be useful considering that the issue of talent management within Qatar is not clearly known; hence, where necessary, it would be ideal to review the theories and redevelop them as and when information becomes clearer.
184
7
CHAPTER SEVEN: QUANTITATIVE DATA
ANALYSIS
7.1
Main Sections of Chapter Seven
This chapter analyses the quantitative data using descriptive statistics as well as non- parametric statistics. It comprises nine major sections and their respective sub- sections. These are as follows: (i) examining the general information from respondents; (ii) evaluating the economic prospects for Qatar, as perceived by respondents; (iii) examining the processes of achieving a KBE; (iv) analysing the processes for talent management; (v) undertaking analyses for hypothesis 1; (vi) examining the data for hypothesis 2; (vii) examining the data for hypothesis 3; (viii) introducing multiple-linear regression on the nine key drivers; and (ix) introducing multiple-linear regression on strategic behaviour.
7.2
Introduction
For the research to establish a framework for talent management that could support the 2030 KBE vision for Qatar, it was envisaged that both qualitative and quantitative data would be sought, as explained in section 6.3.3. From the outset, the design of the data collection was influenced by the qualitative nature of the subject (Gill & Johnson, 2010); however, the questions used to collect the primary information had categorical data sections in the form of Likert scales (Black, 2010; Bryman & Bell, 2007; Hussey & Hussey, 2009). This chapter therefore undertakes quantitative analysis of the primary data collected from the questionnaire survey (see section 6.9), which was designed and distributed electronically via SurveyMonkey.com. This sub- section is structured as follows:
Descriptive Analysis
Section One: General information about the respondent
Section Two: Current economic prospects based on QNV 2030
Section Three: Achieving a knowledge-based economy
185
Non-Parametric Analysis
Chi-Square Test;
Pearson’s and Spearman’s Rank;
Gamma Test;
Multiple Regression.
The questionnaire survey (Appendix A) was designed to have four sections, namely: (i) general information about respondents; (ii) current economic prospects based on Vision 2030; (iii) achieving a knowledge-based economy; and (iv) talent management strategies that support a KBE. Even though the questionnaire survey was split into these four sections, there were a mixture of questions; some had categorical data sets through Likert scales while others did not. The chapter also undertakes a detailed analysis of the results using both descriptive statistics (Black, 2010) and non- parametric statistics (Moore et al., 2011). Descriptive statistics were useful to present the information in graphical form to back up the data analysis and presentation of the results. On the other hand, non-parametric statistics were useful because it allowed the use of categorical data to compare responses as well as predict the prospective impact of the results at influencing the research outcomes (Moore et al., 2011).
As stated earlier in section 6.13, the research adopted SPSS (Statistical Package for Social Sciences) to undertake a detailed quantitative data analysis using non- parametric statistical techniques, which were the Chi-squared test, Pearson’s and Spearman’s Ranks, Gamma, correlation coefficients, and multiple regressions. In order for the research data to maintain high levels of validity and reliability (sections 7.15 and 7.14 respectively), the questionnaire targeted organisations that highly are involved in developing strategies for Qatar’s KBE, which were: the Ministry of Education; Ministry of Development Planning and Statistics; Ministry of Development Administrative Labour and Social Affairs; ICT Qatar; and the Qatar Foundation, which is responsible of research and development and infrastructure projects. Section Four of the questionnaire was restricted to chief executive officers, managers, directors and academics because it was envisaged that respondents who worked in strategic positions were expected to implement and operationalise the Vision 2030 policy (QNV, 2030) in their respective organisations. The chapter
186 concludes that for the Qatar Vision 2030 to transform the economy into a knowledge- based one, there would be a need to undertake a holistic re-focusing of talent management within the economy.