Quantitative Data Collection and Analysis Methodology

Figure 3.1: Decision Tree taken from Ryan and Bernard (2003, p. 102.)

As latent meaning has also long been of interest to the social sciences, once this initial identification of themes at a semantic level had been completed, the data was further analysed at a latent level in order to identify underlying ideas, conceptualisations, and assumptions contained within the data (Glassner and Corzine 1992), again using the decision tree suggested by Ryan and Bernard (2003).

3.5 Quantitative Data Collection and Analysis Methodology

This section describes and discusses the techniques and methods employed to collect and analyse the data from the second quantitative explanatory phase of research, which was undertaken once the initial qualitative phase of research described in the previous section had been completed.

The first stage of this process was the development and testing of an appropriate research instrument that could be used to validate the model conceptualised from the results of the literature review and the findings from the qualitative phase of research. The following section explains the processes that were used, as suggested by Churchill and Iacobucci (2002), Arnold and Reynolds (2003), and DeVellis (2012).

77 3.5.1 Questionnaire Development

The nine-stage process described by Churchill and Iacobucci (2002: Figure 3.2) was adapted as a guideline for developing an effective research instrument.

Figure 3.2: Questionnaire Development and Validation Process taken from Churchill and Iacobucci (2002, p. 315)

Step nine: Pre-test and validate the questionnaire

Step eight: re-examine steps one to seven and revise accordingly.

Step seven: Determine layout and physical characteristics

Step 3: Determine the content of individual questions.

Step four: Form of response Step two: Type of questionnaire and

method of administration.

Step five: Determine the wording of each question

Step six: Determine the sequence of questions

Step one: Identify and specify what information will be sought.

Step One: Identify and specify what information will be sought.

The purpose of this stage is to identify what information it is necessary to collect for the purposes of the study, with the information required usually being primarily dependent upon theory and the conceptual framework of the study (Churchill and Iacobucci, 2002;

DeVellis, 2012). The research instrument used in this study has been developed and designed to capture information relating to each of the constructs and hypotheses

identified in Chapters Twos and Four, which were drawn from the model of trust developed for this study shown in Figure 2.2 on page 39 and discussed in the previous chapter; a review of the academic literature relevant to this study; and the results of the qualitative phase of research. Questions to capture the demographic profiles of respondents were also included, in order to examine and gain an understanding of their moderating effects, together with obtaining a profile of respondents.

Step Two: Type of questionnaire and method of administration.

The purpose of this stage is to identify how the required information will be collected - a decision which is closely linked to the type of information required and influenced by factors such as costs and number of respondents (Churchill and Iacobuci, 2002; Saunders et al., 2009). A self-administered questionnaire was employed as the method for quantitative data collection, as questionnaires can be used to examine and explain causal relationships in explanatory studies (Saunders et al., 2009). Further reasons for selecting a

self-administered questionnaire for this study included the large number of responses needed due to the characteristics of the analytical techniques to be used by this study; the fact that costs would be lower than utilising structured interviews; and the fact that data would need to be collected from a wide geographical area. Both postal and internet-based administration were initially considered, with internet-based administration being selected as the primary method for data collection, for mainly practical reasons, including cost. After discussion with two Independent Financial Advisers (IFAs), it was also considered prudent to produce a small number of paper based surveys that could be posted to potential respondents, as both IFAs were of the opinion that many of their clients were not necessarily computer literate. A further advantage of utilising a self-administered questionnaire is that respondents are less likely to answer questions in a manner that is either socially desirable or in a manner that they believe will please the researcher thereby increasing reliability (Saunders et al., 2009).

Step Three: Determine the content of individual questions.

The purpose of this stage is to develop a set of questions or items to be used in the questionnaire that adequately define and represent each of the constructs, and to ensure content validity (content validity is defined and discussed in Section 3.5.3.1 on page 85) for those potential questions or items. Decisions made in the previous two stages will

influence this stage of questionnaire development (Churchill and Iacobucci, 2002; DeVellis, 2012).

Following the process suggested by Arnold and Reynolds (2003) and DeVellis (2012) for scale development, a review of literature dealing with quantitative studies regarding financial services trust was undertaken in order to identify existing scales that could potentially be used for this study (eg. Sekhon et al., 2014; Hanson and Grimmer, 2007;

Grayson et al., 2008; Ennew et al., 2011; Hansen, 2012a and Hansen, 2012b). However, as

indicated in the literature review, there is a paucity of studies focusing on environmental based trust in the context of the financial advice industry which this study examines. The search for suitable scales was therefore first extended in scope to cover the trust literature in general, and then to a broader range of literature covering a variety of topics which contained potentially useful scales. As a result, 37 papers were identified containing approximately 300 scales with an excess of 1,000 individual items of interest that could potentially be utilised for this study. Each of the potential scales identified were grouped together by latent construct, thereby forming a pool of potential items for each construct.

The individual items for each construct were then reviewed, together with any relevant information provided by the originating study, such as Cronbach alpha scores, loadings, and whether or not the item was subsequently dropped by the authors. Items that did not fit the conceptual definition of the constructs were dropped, as were those found to be ambiguous, irrelevant, too narrow in focus, or unsuitable for modification to the context of this study. Where items were found to be duplicated or identical in meaning to other items, they were either merged into one, or one item was dropped. Several items were also dropped where studies reported particularly poor Cronbach alpha or loading scores which led to that particular item being dropped by the originating study, however several items were kept where scores had been below what is normally considered desirable.

Once this process had been completed, potential items had been identified for 16 of the 22 constructs and hypotheses identified in Chapters Three and Six, however, for two of the 16 constructs where potential items had been found, only one potential item had been found for each. Each of the remaining 14 constructs where items had been found were then examined to ensure that the items for that particular construct covered all aspects and dimensions of the construct identified by the qualitative phase of research. Omissions relating to four constructs were noted.

Potential items for these missing dimensions, together with items for both constructs where only one item had been found and the four constructs where no constructs could be found, were then generated utilising DeVellis’ (2012) guidelines by drawing upon the findings of the qualitative research, ideas from other research and ideas generated from theory (Arnold and Reynolds, 2003).

The resultant items for each scale were then examined by four individuals (An IFA, an academic and a PhD student from a leading UK Business School and one other lay individual who met the criteria for participating in the study) who were asked to examine each scale and comment, including their reasoning, on any technical issues, relevance, applicability, understanding and wording used (DeVellis, 2012). Where necessary further amendments were made to individual items as a result of their comments.

Once completed a q-sort technique was undertaken whereby experts and potential participants group together items according to their similarity (Moore and Benbasast, 1991). This technique is particularly recommended where new scales are being developed in order to assess content validity and identify any particular items where ambiguity may still exist (Moore and Benbasat; Segars and Grove 1998; Bhattacherjee, 2002; Stratman and Roth, 2002).

Five teams of two judges were recruited in order to undertake this exercise. Three academic teams were recruited, two consisting of an academic and PhD student from a leading UK Business School with the other team consisting of two PhD students from other

schools within a leading UK University. A further two lay teams were recruited, one consisting of two administrative staff from a leading UK Business School and the other consisting of two individuals who met the criteria for participation in this study. This range of individual was selected in order to ensure that a range of perceptions was included in the analysis (Moore and Bebasat, 1991).

A set of yellow 2” x 3” cards was prepared with each of the items selected for use in the previous stage printed on one card along with a set of white 3” x 5” cards with each of the constructs identified in Chapter Seven printed on each card.

For the first three rounds of sorting, each of the academic teams was provided with the set of yellow cards, containing individual items which had been randomly shuffled, and were asked to group the cards into construct categories before being asked to write a

description or title for each grouping or construct. This procedure minimizes the risk of

‘interpretational confounding’ which occurs “as the assignment of empirical meaning to an unobserved variable other than the meaning assigned to it by an individual a priori to estimating unknown parameters”, (Burt 1976, p. 4). If the definition written by the judges matches the intent of the construct or scale, then confidence in the validity of the scales increases (Moore and Bebasat, 1991).

Once this first stage had been completed and the results recorded, each academic team was then asked to undertake a second stage, whereby the yellow cards were reshuffled, with each of the white construct cards being placed face up on a table. Each team was then asked to place each of the yellow cards onto a white card with the results, including

disagreements on the placement of cards, between judges being recorded. For the final two rounds each of the non-academic teams were asked to undertake this second stage only.

Once each round had been completed, scales were refined by either re-wording items to better match the relevant construct, or by eliminating items which had been consistently placed on incorrect constructs.

Moore and Benbasat (1991) indicate that a further signifier of content validity was the convergence and divergence of items within categories. Where an item is consistently placed within the same category, it demonstrates convergent validity with that construct and discriminatory validity with the other constructs.

The assessment of the reliability of the q-sort process is often assessed in two ways: firstly, by calculating a value such as Cohen’s Kappa (Cohen, 1960) or Perreault and Leigh’s (1989) measure for the level of agreement between judges when grouping items and, secondly, by measuring the frequency with which judges placed items on the correct construct.

Both Cohen’s Kappa (Cohen 1960) and Perrault and Leigh’s (1989) measure establish whether the degree of agreement between judges is greater than that which would be expected by chance (Stratman and Roth, 2002). It was not possible to calculate such values for this study, as only two disagreements occurred between judges, both of which were in the first round. This deficiency is probably due to the fact that only two judges, instead of the usual four or five, were used for each round of judging, due to difficulty recruiting suitable judges.

The second measure, whereby the frequency that items were placed correctly is calculated, is more of a qualitative analysis than a rigorous quantitative procedure, with no established guidelines for determining what is a good score. However, scales or constructs where a high frequency of correct item placement occurred can be considered to have a high level of construct validity, and have a high potential for good reliability scores (Moore and Benbasat, 1991; Stratman and Roth, 2002).

Step Four: Form of response

Questions relating directly to the scales were multichotomous closed questions with predetermined responses, with a seven point Likert type scale being applied in all cases in order to maintain uniformity. Likert scales are one of the most common question formats and are widely used to measure opinions, beliefs, and attitudes (DeVellis, 2012). Questions relating to both demographic information and the financial adviser used by respondents were also closed with predetermined responses. A small number of open ended questions were also included to allow participants to give their opinion on several issues.

Step Five: Determine the wording of each question

Ensuring that the phrasing and wording of each question is correct is crucial, as poor phrasing can lead respondents to misinterpret questions, leading to incorrect answers or a refusal to answer. One particular area where problems can occur is in the vocabulary used by researchers, as they are often highly educated individuals who are prone to use words that are often not understood by typical respondents (Churchill and Iacobucci, 2002;

Saunders et al., 2009). This was borne in mind when constructing the questions, as was the need to avoid technical terminology and abbreviations used by the financial services industry. Where the use of such terminology or abbreviations was unavoidable, clear explanations and definitions were provided of the meaning. The need to ensure that suitable, understandable, and non-ambiguous wording was being used, that abbreviations used were appropriate and understood, and that questions were not misleading, was a significant driver of the decision to hold a small initial pre-test of the questionnaire, as described under Step Seven.

Step Six: Determine the sequence of questions

Once the wording of the questions has been established, the next step is to determine the sequence in which they will be asked. Churchill and Iacobucci (2002) indicate that the ordering of the questions can be crucial to the success of the research effort. Accordingly, this study will follow their guidelines.

The first few questions should be simple and easy to answer, interesting and

non-threatening. If this is not the case, respondents may decline to complete the questionnaire.

The first three questions were screening questions, which ensured that respondents met the criteria for participation (specifically, over the age of 25, resident in the UK and had used a financial adviser within the previous 12 months), and were followed by questions relating to themselves and their knowledge and opinion of the financial services industry and their financial adviser, before moving on to more detailed questions. The questions relating to the personal profile and demographics of respondents, which can be considered sensitive, were placed at the end of the questionnaire. Wherever possible, questions with similar opening wording were grouped together in order to reduce the overall length of the questionnaire. An open ended question giving respondents an opportunity to comment

upon the questionnaire was placed at the very end, along with a thank you message and the contact details of the researcher. By structuring the questionnaire in this manner, the guidelines recommended by authors such as Churchill and Iacobucci (2002) and Saunders et al., (2009) were adhered to.

Step Seven: Determine the layout and physical characteristics of the questionnaire The physical characteristics of a questionnaire, such as the layout, can affect a variety of factors, including acceptance of it by respondents and their perception of its importance. If such issues arise, they may be detrimental to the cooperation of respondents and their willingness to participate. Furthermore, poor physical design can also have a detrimental effect upon the accuracy of responses and the ease with which they are processed (Churchill and Iacobucci, 2002). Accordingly, care was taken with both the internet based questionnaire and the small number of paper based questionnaires produced to address these issues by following the guidelines suggested by Churchill and Iacobucci (2002) and Saunders et al. (2009), with effort being made to ensure that a professional layout was achieved in order to enhance the credibility and perception of the importance of this study.

For the internet based questionnaire, this entailed providing clear guidelines and

instructions on how to complete the questionnaire, minimising the number of questions on each page to reduce the amount of scrolling required, and providing a ‘progress bar’ on each screen. A decision was also taken at this stage to make the completion of all scale related questions in the internet based questionnaire compulsory, in order to mitigate the risks associated with missing data which are discussed in Section 5.12.1 on page 152.

For the paper-based questionnaire, this entailed producing the questionnaire in booklet form, designed in such way that it was easy to read and without clutter. A freepost address for the return of the completed questionnaire was also prominently displayed on the final page. The University logo was prominently displayed on both, in order to further enhance credibility.

The importance of a well-designed introductory letter in enhancing credibility is also noted by Churchill and Iacobucci (2002). However, as the initial intention of this study was to obtain participants from the client listings of financial advisers, it was felt that the initial approach to potential participants should be made by the financial advisers concerned for two reasons: Firstly, the requirements of the Data Protection Act would have required the financial advisers to request permission form their clients to pass on their contact details to the researcher and, secondly, due to the strong personal nature of the relationship that often exists between clients and their financial adviser, it was felt response rates would be considerably higher if the approach was made by the adviser and that the adviser should therefore structure the request to participate in the manner they felt best. Therefore, a formal introductory letter was not produced, but rather each financial adviser that agreed to assist was provided with a list of points that they were asked to include when requesting participation such as the purpose of the study, guarantees concerning anonymity and confidentiality, and the contact details of the researcher, along with a suggested wording.

Step Eight: Re-examine steps one to seven and revise accordingly.

Whilst Churchill and Iacobucci (2002) include this as a separate step, reviewing and revising the questionnaire was undertaken on an ongoing iterative basis throughout the process of questionnaire development.

However, prior to proceeding to the final step in the development of the questionnaire, a final review was carried out by four individuals (one academic, one PhD student, an IFA who was granting access, and one professional non-academic individual) with amendments being made where necessary.

Step Nine: Pre-test and validate the questionnaire

The next stage of the development is to test and validate the questionnaire under conditions of actual data collection. Such pilot-testing offers an opportunity to detect

In document Trust and the financial adviser: an examination of environmental trust (Page 94-105)