Question and data types

Any question in a questionnaire can be classified as either open or closed (“whether or not the answer can come only from a finite number of possible responses”), spontaneous or prompted (“whether respondents are asked to reply in their own words or given a number of options from which to choose a

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response”), open-ended or pre-coded (“whether the answer is recorded verbatim or against one or more of a number of predetermined answers”). Generally, open questions are asked spontaneously and recorded as either open-ended or pre-coded responses, while closed questions are prompted and pre-coded (Brace 2008:45-46).

The questionnaire used in the present study comprised closed questions only. According to Bryman and Bell (2011:250), closed questions pose five major advantages over open ones.

 The availability of possible answers may help respondents to clarify the

meaning of a question.

 Closed questions are easy for fieldworkers or respondents to complete.

 Closed questions reduce the possibility of variability when recording answers.

 It is easy to process answers for analysis.

 Closed questions improve the comparability of answers, making it easier

to display the relationship between variables and to distinguish between respondents.

There are three basic types of closed questions: dichotomous, multiple-choice, and scale questions (Tustin, Ligthelm, Martins & Van Wyk 2005:397). A

dichotomous question requires the respondent to choose between two

mutually exclusive alternatives (Zikmund & Babin 2010:372). For example, in the current study, this type of question was used to ask the survey participants to indicate their gender.

Multiple-choice questions that provide respondents with more than two

alternatives can be classified into two forms, namely, single-response and multiple-response, depending on whether respondents are asked to select one answer most appropriate to their situation, or as many answers as are

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relevant to their situation (Cooper & Schindler 2006:369). The questionnaire used in the current research included seven multiple-choice, single-response questions that captured seven demographic and travel-related characteristics of the survey participants, that is, age, education, household structure, household income, city of residence, travel frequency, and travel mode.

Finally, a scale question requires the respondent to place the object being rated at some point along a continuum or in one of an ordered range of categories (Tustin et al 2005:401). According to Malhotra (2010:308-314), the widely adopted itemised rating scales are the Likert, semantic differential, and Stapel scales. When constructing any of these scales, the researcher must decide on the number of categories, balanced versus unbalanced choice, odd or even number of categories, forced versus non-forced choice, the nature and degree of verbal description, and the physical form of the scale. Likert scale questions were used in the present study for assessing the latent variables, namely, attitude, subjective norms, perceived control, travel motivation, travel constraints, and visit intention. The survey participants were requested to indicate the degree of agreement or disagreement with each of a number of statements (i.e. items) related to a given variable. Each scale item had five ordered response categories, namely, „strongly disagree (1)‟, „disagree (2)‟, „neutral (3)‟, „agree (4)‟, and „strongly agree (5)‟.

Data can be categorised in terms of the presence or absence of four basic, hierarchical characteristics. From the lowest to the highest level these characteristics are description (the use of unique descriptors or labels), order (the relative sizes or positions of the descriptors), distance (the absolute differences between the descriptors), and origin (fixed beginning or true zero point) (Malhotra 2010:282-284). As shown in Table 2.4, combinations of these characteristics provide four widely recognised types of data – nominal, ordinal, interval, and ratio.

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TABLE 2.4: TYPES OF DATA AND THEIR CHARACTERISTICS

TYPE OF DATA BASIC CHARACTERISTICS

Description Order Distance Origin

Categorical Nominal Yes No No No

Ordinal Yes Yes No No

Continuous Interval Yes Yes Yes No

Ratio Yes Yes Yes Yes

Source: Adapted from Burns & Bush (2006:276)

Nominal data allow the researcher to identify and classify objects (variables or

cases) in possession of the characteristic of description; ordinal data permit the researcher to rank the objects in possession of the characteristic of order (and description); interval data enable the researcher to compare the differences between the objects with the characteristic of distance (and order and description); and ratio data allow the researcher to compute the ratios between the objects with the characteristic of origin (and distance, order, and description). In general, nominal and ordinal data are categorical data, while interval and ratio data are continuous data (Burns & Bush 2006:276-278; Cooper & Schindler 2006:342-346). In the current context, the questionnaire was designed to collect nominal, ordinal, and interval data. More specifically, gender, household structure, travel mode, and city of residence were treated as nominal data; age, education, household income, and travel frequency were considered as ordinal data; and visit intention and its various predictors were viewed as interval data.

To summarise, the questionnaire constructed for this study collected nominal, ordinal, and interval data by using dichotomous, multiple-choice, and scale questions. The questionnaire was organised in the following order: the cover letter (discussed in Section 2.3.3), the scales of travel motivation, travel constraints, attitude, subjective norms, perceived control, and visit intention (discussed in the next section), and demographic and travel-related questions

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(also referred to as classification questions). Brace (2008:44) advocates that classification questions are invaluable for cross-analysis purposes, although they rarely relate directly to the study‟s subject matter.