Perspectivas Estructurales

3.1 INTRODUCTION

Maintaining academic integrity in nursing education is integral to the development of ethical practitioners in the nursing profession. This research explored the status of academic integrity among nursing students in a nursing education institution in the Western Cape. The research investigated the influence of age, gender, home language and year of study on academic dishonesty. The prevalence of academic dishonesty, the attitudes of nursing students towards cheating behaviour, the influence of peer behaviour and the control of academic dishonesty were also investigated.

3.2 RESEARCH APPROACH AND DESIGN

Fouché and De Vos (cited in De Vos et al., 2005:101) state that the research topic determines the decision whether to utilise a quantitative, qualitative or combined qualitative-quantitative research approach. Burns and Grove (2009:22) define quantitative research as “a formal, objective, systematic process in which numerical data are used to obtain information about the world”. This approach is grounded in the philosophy of logical positivism and it therefore focuses on finding the truth through the objective measurement of reality (Burns & Grove, 2009:23).

According to Fortune and Reid (1993, quoted in De Vos et al., 2005:73) quantitative studies focus on specific questions throughout the investigation and specific variables are measured. These variables are quantified by means of rating scales or frequency counts. Standardised procedures, for example the completion of the same questionnaire by all the participants, are utilised to collect data. In this way, the researcher takes on the role of “an objective observer” with limited involvement in the study phenomena (Fortune & Reid, 1993, quoted in De Vos et al., 2005:73).

As a result of the sensitivity associated with studying academic dishonesty, most of the studies done in this regard are quantitative in nature, with self-reporting surveys the most common method used (Hilbert, 1988:163; Lim & See, 2001:265; McCabe, 2009:616; McCabe & Treviño, 1997:385; Newstead et al., 1996:231). Accordingly, the researcher also decided on a quantitative research approach for this study. The quantitative research approach was further justified by the fact that in view of the sensitive nature of the topic, the researcher required answers to specific questions, quantification of variables, a large number of participants, objectivity in collection and analysis of data and a standardised method of data collection.

In referring to the research design as the “blue print” according to which a study is planned, Burns and Grove (2009:236) mention that its purpose is to provide control over the study to maximise the validity of the findings. Non-experimental research designs, for example descriptive and correlational designs, are often utilised in nursing research where phenomena are studied in their natural environment without any manipulation of the variables (Burns & Grove, 2009:237). According to Brink (2006:102) the purpose of non-experimental research is to describe phenomena, and to examine and describe relationships among the variables. Descriptive designs are utilised in studies where more information in a particular field is required to provide a picture of the phenomenon as it occurs naturally (Burns & Grove, 2009:237). In such designs the variables are described to answer the research question (Brink, 2006:102). The complexity of descriptive designs depends upon the number of variables involved. Since causality is not established in descriptive designs, no dependent and independent variables are identified (Burns & Grove, 2009:237).

According to Brink (2006:11) the research problem and the existing knowledge regarding the research topic dictate the choice of research design. In this study a descriptive survey design was utilised to investigate the extent of academic dishonesty among nursing students at a nursing education institution in the Western Cape. The researcher opted for this type of research design due to the sensitive nature of this issue and to optimise the collection of honest data by means of questionnaires. Furthermore, the phenomenon of academic dishonesty could be studied as it occurred naturally without any manipulation of variables.

3.3 POPULATION AND SAMPLING

The target population comprises all the individuals that meet the criteria for inclusion in the sample (Burns & Grove, 2009:343). The inclusion criteria, that is, those characteristics that a respondent must have to be included in the study (Burns & Grove, 2007:325), were that the respondents had to have done at least one theory examination, one theory assignment and one theory test at this educational institution. The target population therefore included all the pre-registration nursing students in the second-(N=319), third- (N=199) and fourth-year (N=170) groups at a specific nursing education institution in the Western Cape. The first-year student group was not considered for inclusion in the sample on the grounds that they had not yet been exposed to at least three assessment tasks at the nursing education institution. The estimated size of the target population was 688 students (N=688). The purpose of the sampling process is to select a representative group of respondents from the target population for inclusion in the study (Brink, 2006:124). Representativeness means that the sample group should have more or less the same characteristics as the larger population in order for the findings of the study to be generalised to the larger population (Strydom, cited in De Vos et al., 2005:196). The highest degree of representativeness is achieved through random sampling, where each member of the population has an equal chance of being selected for the sample (Strydom, cited in De Vos et al., 2005:197). The researcher had originally planned to utilise a systematic random sampling technique for the selection of respondents. However, this method has the major drawback that a sampling frame containing the names of all the respondents is utilised. This may cause respondents to question whether their complete anonymity is indeed ensured. The researcher was alerted to this problem when the respondents who participated in the pilot study indicated that they felt uncomfortable with the fact that their names were known to the researcher, although complete anonymity had been guaranteed. The researcher therefore preferred to opt for a non-random convenience sampling technique where respondents were included in the study on the grounds that they were “in the right place at the right time” (Burns & Grove, 2009:353), meaning in a certain class at a certain time. In this manner the anonymity of the respondents was enhanced and the chances of securing honest data regarding a sensitive topic were increased. A

disadvantage of this sampling method is that all the respondents do not have an equal opportunity to be included in the sample, meaning that there is a chance that the sample will not be representative of the population (Burns & Grove, 2009:353). There are no definitive prescriptions about the adequate size of a sample for a study, but larger samples are often used in quantitative studies (Brink, 2006:135). Stoker’s table (1985, quoted in De Vos et al., 2005:196) provided some guidelines for the sample size of a study (see Table 3.1). According to this table a study with a population of 500 respondents is adequately represented by a sample of 20% (100 respondents). However, in this study, as a result of the non-random sampling method, a sample of 80% was selected in an effort to increase representativeness. Basavanthappa (2007:300) also indicated that the danger of sample bias can be reduced with a large representative sample. In addition, the decision to enlarge the sample to 80% was based on previous studies on academic dishonesty which reported poor response rates, ranging from as low as 13% (McCabe et al., 2006:297) to 35% (Jordan, 2001:237). The low response rate of 39% in the pilot study for this study was in line with these findings. According to Burns and Grove (2009:409) the representativeness of a study may be questioned if the response rate is lower than 50%. Increasing the sample to 80% would ensure that enough data was generated for analysis and interpretation. Accordingly, a sample of 80% (n=550) was obtained from each of the above-mentioned student groups in the target population, namely second-year (n=255), third-year (n=159) and fourth-year (n=136) students.

Table 3.1

Guidelines for sampling

Population Percentage suggested

(%)