Chapter 3: Methodology
3.2. Research design
3.2.1. Cross-sectional Research Design
A cross-sectional study was employed in the current study. A cross-sectional study refers to a research design that makes use of different groups or between-subjects approach (Goodwin, 2002). For instance, this design allowed for comparisons across five different age groups at a time when investigating risk-taking behaviour patterns; whereas a longitudinal study would have focused on studying one age group over a long period of time in order to determine risk-taking behaviour patterns of that particular age group. The cross-sectional study is time efficient as opposed to the longitudinal study, which usually lasts for years (Goodwin, 2002). Therefore, it was relevant and fitting for the current study to make use of the cross-sectional study because of the need to complete this research project within a shorter period.
A prominent short-coming regarding the cross-sectional study is its failure to make predictions of changes that occur over a period of time (Flisher et al., 1993a). For instance, a cross-sectional study would confirm that the individual engages in risk-taking behaviour but would not predict a change in that behaviour overtime. Another short-coming pertains to its inability to acknowledge variables that influence the outcomes of a study (That is, culture, environment, and age groups) when comparing different groups (Goodwin, 2002). Given such limitations, interpretations of results should be carefully made.
3.2.2. Correlation-based Research
This current study is also a correlation-based research study. A correlation study is defined as an approach that focuses on the investigation of relationships between two or more variables (Aron & Aron, 1997; Goodwin, 2002; Hofstee, 2010). Self-esteem and risk-taking behaviour are variables under investigation in this study. A distinction between positive and negative correlation is essential for describing the nature of a relationship (Aron & Aron, 1997).
A positive correlation is where high scores go together with high scores, and then low with low and moderate with moderate scores. For example, a positive correlation between alcohol and unsafe sex is that, the higher the consumption of alcohol the higher the chance of unsafe sex practice. On the contrary, a negative correlation would mean that high scores will go together with low scores and that the variables will usually go in opposite directions (Goodwin, 2002). For example, a correlation study between self-esteem and risk-taking behaviour would indicate that the higher the self-esteem, the lower the level of risk-taking behaviour. However, it is also possible for two variables to have no relations with each other, whereby those variables are independent of each other (Aron & Aron, 1997).
It is important to note that correlation does not necessarily mean or refers to causation, and most researchers warn against such conclusions (Denscombe, 2007; Gravetter & Forzano, 2009; Hofstee, 2010). According to Goodwin (2002), a finding that there is a correlation between two variables does not imply that one variable is causing the other variable to occur because the presence of a third variable could be accountable for the correlation found between those two variables.
For instance, a correlation between self-esteem and risk-taking behaviour would not necessarily mean that having a poor self-esteem causes risk-taking behaviour because other excluded factors like environment, peer pressure and personality could also be responsible for risk-taking behaviour. Therefore, precautions need to be taken when interpreting findings for a correlation-based study, especially with regards to generalisations and over-simplification of complex relationship (Hofstee, 2010). Correlation-based studies are significant for behaviour predictions, for laying foundations for further and future research, and they are usually implemented in cases where experimental surveys prove to be limited in terms of application due to ethical and practical concerns (Goodwin, 2002; Hofstee, 2010).
3.2.3. Quantitative Research
This study further employed the quantitative research method because of its correlational nature. Quantitative research is a systematic and objective method which makes use of numerical data from selected subgroups of a population in order to generalise the findings to the population that is being studied (K. Maree, 2010; Goodwin, 2002). This method is designed to examine (or correlate) variables (that is, self-esteem & risk-taking behaviour) which usually differ in quantity, like size, duration and amount (Gravetter & Forzano, 2009).
The advantages of using a quantitative study are many. Below is a list of a few of these advantages as described by Denscombe (2007):
A quantitative study is associated with large-scale studies where the results can be generalised to the population being studied. Also, large amount of data is quickly analysed given proper plans.
It provides a concrete foundation for description and analysis, since it is largely associated with analysis.
Tables and charts employed by this method provide a concise manner of organising data and communicating the findings to others in a less complex way than in qualitative research.
It provides “objective” data due to the detachment of the researcher within the actual study and analysis.
It is important to note that this method is not based on the premise that theory and methods will emerge during the course of the research as in qualitative research, because quantitative research is more specific and predetermined (Gravetter & Forzano, 2009). On the one hand, it is an advantage to use a specific and predetermined method because it gives a study a sense of direction and helps keep focus. On the other hand, it limits the inclusion of other relevant and necessary considerations once started (Stevenson, 2001).