Co-crystal formation during F6TCNNQ deposition

Validity and reliability (rigour) are emerging as salient measures for evaluating the quality of research. Neuman (2011) cites reliability and validity as ideas that help to establish the

‘credibility’ of findings. Reliability aims towards the consistency or replication of research findings in similar conditions, while validity evaluates the truthfulness of findings. The latter can be demonstrated in three ways: the validity of selected measures or ‘construct validity’,

‘internal validity’ and ‘external validity’. Most often, validity is associated with the

‘operationalisation’ of concepts, which is commonly used in quantitative research (Mason 2002). Although reliability and validity are treated separately in quantitative studies, these terms are not viewed separately in qualitative research. Instead, terminology that encompasses both, such as credibility, transferability, trustworthiness or dependability, and confirmability are used (Hoepfl 1997, Riege 2003). Internal validity is used for establishing causal relationships and external validity deals with the generalisation of findings (Neuman 2011). Generalisability aims towards making general conclusions/claims based on the research findings, rather than them being particular to the research context. However, chance, bias and confounding are the three main threats to validity. Miles and Huberman (1994) identify the essential questions that need to be asked in the domains of reliability, internal validity and external validity (see Table 3-6). Yin (2003) explains two types of generalisation: statistical generalisations and analytic generalisations. He further differentiates that the statistical generalisation is established by an inference made about a population on the basis of empirical data collected about a sample and that the analytic

generalisation is employed as a framework with which to collate the empirical results of the case study. This study exploited analytical generalisation in the case studies and statistical generalisation in the web-based surveys. However, generalisability is a particular concern for a single case study design (Saunders et al. 2007). Attention was paid to explain the validity and reliability issues particular to case study research, as this investigation was fundamentally supported by two main cases. Excluding external validity, the other three case study design tests (construct validity, internal validity and reliability) were undertaken to check the confirmability, credibility and dependability/trustworthiness of the findings. Table 3-7 discusses the techniques for evaluating validity and reliability in case study research.

Table 3-6: Key considerations of validity and reliability

Component Reliability Internal validity External validity Research

question

Clear? Matches with the research

design?

Meaningful? Defines the scope and delimitations?

In this study, each component is discussed in particular sections of this chapter.

Table 3-7: Techniques for evaluating validity and reliability in case study research

Case study design tests

Corresponding design tests

Case study techniques Qualitative techniques Phase of research in which techniques occur

 Use multiple sources of evidence

 Establish chain of evidence

 Have key informants review draft case study report

Internal validity  Credibility  Do within-case analysis, then cross-case pattern matching

 Do explanation building

 Assure internal coherence of findings and concepts are systematically related

Case study design tests

Corresponding design tests

Case study techniques Qualitative techniques Phase of research in which techniques occur

External validity  Transferability  Use replication logic in multiple case studies

 Define scope and boundaries of

reasonable analytical generalisation for the research

 Compare evidence with extant literature

 Predetermined questions

Reliability  Dependability  Give full account of theories and ideas

 Assure congruence between research issues and features of study design

 Develop and refine case study protocol

 Use multiple researchers

 Record observations and actions as concretely as possible

 Use case study protocol

 Record data, mechanically develop case study database

 Assure meaningful parallelism of findings across multiple data sources

3.4.6.1 Triangulation

As illustrated in Table 3-7, ‘triangulation’ is a popular technique for testing the credibility of findings in qualitative research. On the other hand, it is identified as a very powerful technique to gain insights and results, assisting in making inferences and drawing conclusions. Simply, triangulation is a ‘means of cross-checking the relevance and significance of issues or testing out arguments and perspectives from different angles to generate and strengthen evidence in support of key claims’ (Simons 2009 p.129). In a way, it is a ‘validity procedure where researchers search for convergence among multiple and different sources of information to form themes or categories in a study’ (Creswell and Miller 2000 p.126). The literature reveals four types of triangulation (Love et al. 2002 cited Denzin 1978):

 Data triangulation, where data is collected at different times or from different sources;

 Investigator triangulation, where different researchers independently collect and analyse data on the same phenomenon and ultimately compare results;

 Methodological triangulation, where multiple methods of data collection and analysis are used; and

 Interdisciplinary triangulation, where the research process is informed not only for example by psychology, but also by other disciplines such as economics, law and sociology.

This study exploited the method of triangulation to find the credibility (the internal validity) of the results. This method can be used to approach the research question from different angles (Mason 2002). In one way, it is a strong method; however, the whole process takes considerably much more time than a single method. The literature suggests that the rationale of multi-method research is underpinned by the principle of triangulation, which implies that researchers should seek to ensure that they are not over reliant on a single research method and should instead employ more than one measurement procedure when investigating a research problem (Bryman 2008). More specifically, this study used multiple methods to cross-check the internal validity of the findings. Initially, interviews were undertaken and then a case study method was exploited for in-depth evaluation. In addition, web-based questionnaire surveys were undertaken to clarify issues on design parameters and economic considerations for adaptability in buildings.

The data exploited in this study was obtained from different sources. For example, the building maps of Loughborough over the last century were collected from the Leicester Records Office and Charnwood Borough Council. The information was reassessed by a professional at Charnwood Borough Council and the Leicester Planning Authority to improve reliability and generalisability. The cost data for this analysis was obtained from the Building Cost Information Service. This study also exploited the cost information to identify cost-significant building elements. No major deviations (outliers) could be seen in the unit costs of the selected buildings.

3.4.6.2 Analytical generalisation

The theory for case studies is characterised as analytical generalisation and it is frequently adopted in qualitative research. It aims to test the validity of a research outcome against the theoretical network that surrounds the phenomenon and the research outcome (Yin 1994).

This study used analytical generalisation to generalise the outcome of the case studies, which is that building change occurs over time. Yin (2009) explains that the previously developed theory is used as a template with which to compare the empirical results of the case study. If two or more cases are shown to support the same theory, replication may be claimed. The existing theories on building change patterns and their adaptations were used with some empirical evidence (interviews and secondary data analysis) to generalise the phenomenon.

3.4.6.3 Statistical generalisation

Statistical generalisation is making an inference about a population on the basis of empirical data collected about a sample from that universe (Yin 2009). This research used statistical generalisation to generalise the findings of WBS2 and WBS3. As noted previously, the respondents for each survey sample (architects and quantity surveyors) were grouped into three categories (early respondents, late respondents and non-respondents) and then early and late respondents were compared.

3.5 Summary

This chapter has outlined the research methodology that was adopted to gain well-informed insights into this scientific investigation. The research aim was to identify the economic considerations for change of use in buildings within the wider context of adaptability over the lifecycle aspects. The adopted research design was a multi-method approach, which was further explained in terms of purpose, type of investigation and temporal aspects. The dominant purpose of this study was explorative in nature; however, some aspects of descriptive and explanatory traditions were adopted in the research objectives. The ultimate aim was to explore the economic considerations for change of use in buildings. This required understanding of design, cost and benefit aspects of change of use. Thus, empirical evidence-based practical investigation (applied) was undertaken. Holistically, the study exploited a multi-method approach and a literature review, case studies, interviews, web-based questionnaire surveys, archival analysis, secondary data analysis and workshops were used to gather the data for the research development and validation stages. The method of ‘triangulation’ was used to evaluate the quality and rigour (reliability and validity) of the research.

Chapter Four

4. ADAPTATION – A HISTORICAL