Chapter 5 Methodology Methodology
5.2. Research Design
5.2.1. Quantitative approach
Quantitative research emerged around 1250 A.D. and was driven by investigators with the need to quantify data (Williams, 2007). Historically, according to Punch (2003), experimental designs (where the researcher manipulates one or more variables in order to study the effects on other variables) were used and dominated quantitative approaches. The same author (Punch, 2003) states, however, that there has been a move away from a narrow concentration on experimental methods to more widely applicable approaches using non- experimental methods such as social observations of naturally occurring variation in variables. Furthermore, there are certain behaviours that cannot be studied in
experimental situations and these behaviours aptly lend themselves to observational or quantitative research designs (Jackson, 2012). For example, Brown, Cozby, Kee and Worden (1999) state that it would be impractical to manipulate child-rearing practices for research purposes. Even if it were possible to assign parents randomly to two child-rearing conditions, such as using withdrawal of love versus physical types of punishment, Brown et al. (1999) suggest that such manipulation would be unethical. Instead of manipulating variables such as child-rearing practices, researchers using a quantitative approach would observe them in a quantifiable manner as they occur in their natural settings. This also shows that in a quantitative study data does not have to be naturally available in a quantitative form. To substantiate this, Muijs (2011) argues that a non- quantitative phenomenon such as a person’s beliefs can be turned into quantitative data through measurement instruments, for example, using Likert scales.
Different researchers define quantitative research differently. Kothari (2004) and Punch (2003) define a quantitative approach as research that involves the generation of numerical data which can be subjected to rigorous statistical analysis in a formal and rigid fashion. Casebeer and Verhoef (1997), as well as Sukamolson (2005), provide a much broader definition of quantitative research as the numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect. From these definitions, it becomes apparent that quantitative studies emphasise the measurement and analysis of relationships between variables (Casebeer &
Verhoef, 1997). According to Gavin (2008), a major objective in quantitative research is to organise, summarise, and simplify information about data collected in more manageable forms such as tables and graphs, and, even more usefully, as statistical summaries. It is important to note that use of the concept ‘quantitative’
does not describe a single research design. Various research designs exist to conduct different types of quantitative research. Among them, those that are relevant and are used in this research are cross-sectional, correlational and survey designs. A discussion of these designs will follow shortly.
According to Johnson and Onwuegbuzie (2004), as well as Neuman (2000), the quantitative approach has a number of advantages such as being useful to studying large numbers of people, data analysis is relatively less time-consuming
when using statistical software, and data collection using some quantitative methods such as telephone interviews and self-report measures is relatively quick.
The number of phenomena that can be studied in a single quantitative project using questionnaires is vast, thus making quantitative research quite flexible and economical (Sukamolson, 2005). Struwig and Stead (2001) add that the strength of quantitative research lies in its ability to generalise results beyond the confines of the research sample. When applying quantitative methods, Casebeer and Verhoef (1997) propose that numerical estimation and statistical inference from a generalisable sample are often used in relation to a larger population of interest.
This is possible when data is based on a random sample of sufficient size (Johnson
& Onwuegbuzie, 2004). Quantitative research, according to Sukamolson (2005), is also useful to quantify opinions, attitudes and behaviours and to find out how the whole population may feel about a certain issue.
In spite of their distinguishing strengths, Kura (2012) states that quantitative approaches may be criticised for their lack of rigour, for ignoring the reality of the social world of the researched, for neglecting the socio-cultural contexts of phenomena, and for counting and analysing variables, because numbers do not provide any detailed explanation of a research phenomenon. Quantitative research is also usually critiqued for giving a weak theoretical account of how constructs are derived (Struwig & Stead, 2001). In addition, with regard to this weakness, Johnson and Onwuegbuzie (2004) state that the quantitative researcher may miss out on phenomena that are occurring because of the focus on hypothesis-testing rather than on theory- or hypothesis-generation. Therefore, it is apparent that certain questions may be better suited to be answered using quantitative methods because quantitative research is essentially about collecting numerical data (Sukamolson, 2005). It is thus essential to use the right data collection tools, but even more important to use the correct research design to suit the aims of a study.
5.2.2. Cross-sectional design.
In a cross-sectional design, data is collected from more than one participant using a survey at a single point in time (Bryman, 2008; Dunn, 2010;
Punch, 2003), although the recruitment of participants may take place across a longer period of time (Sedgwick, 2014). This implies that information gathered in cross-sectional studies represents what is going on at only one point in time.
Studying change of behaviour over time using cross-sectional design would, therefore, be very difficult. To study change over time, according to Bethlehem (1999), would mean repeating a survey at a number of different points in time.
Doing this, however, is likely to encroach in the area of longitudinal surveys. In a longitudinal study participants are observed at multiple time points thereby allowing trends in an outcome to be monitored over time (Sedgwick, 2014).
Participants in this study completed the questionnaires once only, and no follow- up activities were scheduled. While cross-sectional designs are usually conducted to estimate the prevalence (or frequency) in a population at a given point in time (Mann, 2003), they could also be used to collect data on individual characteristics (Levin, 2006) or level of a particular attribute. Some authors (Rubin, Amlot, Page &
Wessely, 2009; ul Haq, Hassadi, Shafie, Forooqui & Aljadhey, 2012) have used cross- sectional design to study the knowledge and attitudes of their sample of various health-related topics.
According to Mann (2003), and Salkind (2010), cross-sectional studies are relatively inexpensive and quick to conduct because researchers can test different demographic variables at the same time. They are usually based on a questionnaire survey and allow for the possibility of assessing more than one outcome (Sedgwick, 2014). The generalisability of cross-sectional studies is usually good because they tend to be representative of the population being studied. To increase the likelihood of making inferences about the population as a whole, Zheng (2015) suggests that the study sample must be selected at random. Cross- sectional studies are also useful for public health planning, understanding disease aetiology and for the generation of hypotheses (Levin, 2006).
From its description, it is apparent that a cross-sectional study has an inherent problem due to the fact that conclusions are based on data gathered at one time (Babbie & Mouton, 2001). It would, therefore, be difficult to make inferences about changes that occur over time when using a cross-sectional design (Salkind, 2010). Therefore, only an association, and not causation, can be inferred from a cross-sectional study (Sedgwick, 2014). However, Bryman (2008) suggests that this problem could be solved by writing detailed methodology to allow for replication of the study at another time. Sedgwick (2014) also agrees that cross-sectional studies are sometimes repeated at different times to assess trends over time. However, Sedgwick (2014) cautions that if different participants are
included at each point in time it may be difficult to assess whether changes observed reflect a trend or simply the differences between different groups of participants sampled from the population. A further limitation, according to Mann (2012), is that rare conditions cannot be studied efficiently using cross-sectional studies because even in a large sample there may be no one diagnosed with the disease being studied.