Quantitative and Qualitative Research Approaches

In general, quantitative approaches place emphasis on developing an approximation of the situation based on a sample of subjects using survey methods and applying statistical techniques to distinguish overall patterns. Instead of drawing conclusions subjectively through perception, consideration or intuition, it is fairly easy to survey people using more scientific and objective methods. This leads to more quantitative

techniques and operations can be applied. Accordingly, clearer results can be obtained and generalizations can be made.

Qualitative approaches on the other hand focus on understanding a particular situation and examining or interpreting the statistically significant factors that are difficult to quantify using traditional quantitative approaches. In most problems related to people’s perceptions, attitudes and behaviour, qualitative approaches are used as a supplementary method to a quantitative approach in order to understand more clearly the subject matter (Clifton and Handy, 2003; Ortúzar and Willumsen, 2011). Qualitative approaches include in-depth interviews, brainstorming, paired interviews, telephone interviews, participant observation, open-ended questioning and focus groups. Methods such as focus groups, interviews, and participant-observer techniques (Clifton and Handy, 2003) and ‘action research’ transport survey methods (Lucas, 2013) can be used in conjunction with quantitative approaches or on their own to fill the gaps left by quantitative techniques.

Prior to the work of Grosvenor (2000), Pendyala and Bricka (2006), and Clifton (2013), studies focused on the development and application of social survey methods for understanding travel behaviour by using quantitative and qualitative survey instruments. Qualitative designs are more flexible and aim to explore what people think and how they behave and this usually involves knowledge-gathering and observation (Kumar, 2011). For instance, Beirão and Cabral (2007) and Line et al. (2010) used qualitative approaches to understand attitudes towards public bus transport and private car use. Meanwhile, Carrasco and Lucas (2015) suggested that when measuring attitudes concerning, and perceptions of, people’s travel choices, both quantitative and qualitative approaches are useful methods.

It has been noted that this type of research can be designed using both the quantitative and qualitative types of methods, referred to as mixed methods. In many cases, quantitative and qualitative approaches are ultimately complementary techniques, rather than alternatives. However, considering the objective of this research, which seeks to investigate the characteristics of group(s) of people, based on their socio-demographic characteristics, who may have more potential to shift towards sustainable

effective way to generate attitudinal perceptual data that are predominantly categorical in nature, whilst the quantitative method mostly deals with numerical data. Sections 3.2.1 and 3.2.2 are used to explain the details of attitudinal variables and categorical data respectively.

3.2.1 Attitudinal variables

Attitudinal variables are collected by using questionnaire surveys or structured/semi-structured interviews designed to measure respondents’ opinions on a particular subject, either products or services, or to identify their feelings about something (Morey, 2006). The attitudinal variables are generally combined with other types of data, such as the socio-demographic characteristics of the respondents, in order to obtain a more in-depth understanding of a subject.

Qualitative research has long been criticised for its lack of scientific rigour and subjective interpretation (Sandelowski, 1986). In order to avoid these weaknesses, there is a growing trend of using attitudinal questionnaire surveys combined with numerical data to provide a richer understanding of “attitude-caused travel behaviour” (Clifton and Handy 2001). As suggested in psychological research, attitudes are powerful elements of people’s actions (Kollmuss and Agyeman, 2002; Howarth, 2006) and taking these into account is crucial for the success of new strategies designed to reduce private car driving and promote pro-environmental travel behaviour (Nilsson and Küller, 2000).

3.2.2 Categorical data

In many fields such as psychology, science and transportation, categorical variables are commonly used when designing surveys. A set of non-overlapping variables are called categorical variables (Salkind, 2010). In transportation research, multinomial or binary logistic regression seem to be popular, especially when analysing categorical data (Al-Ghamdi, 2002; Li et al., 2016) and in log-linear modelling (Jang, 2006; Olmuş and Erbaş, 2012; Samimi, 2012). User preferences for mode choice, journey related variables,

socio-A measurement scale is ordinal if the categories can be ranked, such as perception variables with options ranging from “strongly agree” to “strongly disagree”, or “not important” to “very important”. However, a measurement scale is nominal if the categories have no ordering, such as colour (for example: red, blue, and green) and gender (male and female). Rating scales are normally used to define categorical variables over a range from lower to upper values. Several kinds of evaluation measures have been developed to rate attitudes, and the most commonly used is the Likert scale.

Accordingly, responses using Likert scales are usually treated as ordinal data (Bertram, 2006), where respondents can give a numerical response within a range of incremental scores. These are often on a scale with a range of for example 1 to 5 or 1 to 7 where 1 is labelled “strongly agree” and the upper value “strongly disagree”. An odd number of intervals is given so that a respondent can be neutral in their responses. In general, this technique is easy to manage and adopt and is a suitable method for gathering numerical data for non-physical latent variables such as of respondents’ awareness, perceptions, opinions, attitudes, intentions and preferences.

Latent variables are referred to as variables that cannot be directly observed. The variables of this type therefore are used in the questionnaires as the indicators to measure the perceptions and attitudes of respondents with respect to their preferences and intentions.