Drastic Depreciation in Performance of University Students - Research Proposal Example

WK7 Research Design Research involves the critical analysis of pre-existing conclusions and theories with regard to newly discovered facts with the intent of continuous search for new knowledge. Moreover, research is the complete process of arriving at effective alternatives and solutions to existent problems through the systematic collection, analysis and subsequent interpretation of data. Relevantly, this research aims at obtaining data based on existing evidence on the factors contributing to the depreciating performance of students in universities. The preset time frame of this research is three-four months since this is the period when the students are in semession. This research extensively purposes at obtaining new knowledge of the drastic depreciation in performance of university students through significant utilization of relevant concepts. The attained results will assist in the description of the problem, development of countering alternatives and provision of knowledge and skill useful in decision-making for the university managements. Furthermore, the results will make it possible to make future performance predictions hence contributing to the possibility of controlling university performances. A research design simply includes a strategy applicable for a specific study and the plan of actively exercising the strategy. The research methodologies in use will include quantitative designs as well as qualitative designs individually thus resulting to a combination of both. Qualitative research design mainly involves techniques and measures, which do not eventually produce discrete numerical data (Creswell, 1997). Qualitative data collection incorporates such methods as direct observation by the researcher, active participation or interviews. Qualitative research illustrates relevance in this research as it aims at achieving an in depth understanding of the factors contributing to the depreciation in university students’ performance. Inclusion of arrays of interpretative techniques to describe and translate the data collected will further improve the quality of the data (Maxwell, 2007). Subsequently, after collection of data through the qualitative research design, the data analysis will involve; content analysis of written or recorded data as well as data drawn from the participants’ expressions and behavioral observations. On the other hand, quantitative research design utilizes techniques and measures that in turn produce discrete numerical thus quantifiable data. Hence, this design will integrate computer analysis of data collected at some point, perhaps through Excel worksheets. Its advantage over the qualitative design is that it critically ensures consistency and no prior preparation is required thus notably avoiding the participant’s biasing. However, an integration of both designs increases the data’s credibility since no stone remains unturned. Firstly, the use of both allows for better assessing of the diverse objectives available as both methods supplement each other’s ability. Nonetheless, the availability of bias in both methods calls for an integration of both. Significant suppression of bias evolves with incorporation of both methods together as each design checks the other’s bias. A sample is a selected subset of the particular population under research. In this case, the population under research includes university students and following to the high number of students a sample ought to be selected. The sample selection will be in a manner that it directly represents the entirety of the universities in which performance has significantly depreciated recently. In this regard, sampling is therefore the process applied in the selection of a representative sample from a targeted population. Sampling takes place with the intent of reducing the cost of collecting data in that a smaller population requires fewer expenses for data collection as compared to the entire population. Sampling also reduces the amount of time used in data collection thus consequently speeding up the data collection process. The availability of classifiable population elements backed by the physical impossibility of checking all members of the population further encourage sampling. Most importantly, sampling ensures heightened accuracy of the results since there is better concentration on the smaller sample. This research therefore incorporates both probabilistic and non-probabilistic sampling methods intended at producing the best sample from the large population. Firstly, the simple random technique avails students for participation by giving each of them an equal chance of participation. Although this technique is easy to implement, it poses threats to the research since it best fits larger sample sizes thus resulting to larger errors. Secondly, the stratified random sampling involves sub division of the university students into sub groups called strata and then selecting a sample from these strata. The selected sample will therefore represent each stratum thus directly representing the targeted population. The best strata would include subdivisions representative of each university under target. Resultantly this will ensure representation of almost all universities. This strategy ensures controlled numbers in each strata thus increased statistical efficiency especially in the quantitative analysis design. This sampling method also allows for the use of different data collection methods in each strata this increasing the data’s reliability. However, diverse selection methods might result to increased uncorrectable errors as this method is relatively expensive. Furthermore, under the non-probabilistic category, quota sampling technique will give room for purposive selection of students to fit the identified quotas for instance, the level of study in the university, the degree course in pursue or the gender of the students illustrating worse performance. This method ensures receipt of data from each level of the university thus on combination the data shows diversity compared to data obtained from one level of students such as second years alone. However, several factors have to be considered in the sampling process such as budgetary constraints, the sampling procedure to be applied and the size of sample projected. These considerations will in turn ensure the research is not strained beyond its potential ability. The sources of bias in the selected sample may result from lack of interest of the selected students for participation. The diversity in perception contributes to this since some students may not understand the need for this research therefore failing to participate in events like interviews or filling of questionnaires. Secondly, bias may arise from pressure on the selected students by fellow students for instance, drug users. Evidently, use of drugs is one of the main variables contributing to depreciated performance. These students will fear exposure thus pressure or even threaten the participating sample to give false information thus illustrating bias. Nonetheless, problematic encounters projected include lack of participants, increased expenses, time consumption, inaccuracy of the data obtained and diversity in the sampling units. At the outset, lack of participants may arise from the busy schedules of the students or as explained earlier, pressuring threats from possible victims. This will radically decrease the possible sources of primary data thus reducing its credibility. Consequently, this will contribute to increased time consumption than estimated to complete the research. Hasty conduction may also result to increase errors. Furthermore, the combination of research designs as well as sampling strategies may lead to increased expenses. Reliability and validity of data both serve the intent of increasing the quality thus credibility of research. Reliability and validity rely solely on the accuracy of the data collection procedures as they measure the relevance and correctness of the data (Salkind, 2010). Reliability is the extent to which any applied procedures in research yield similar results even on repeated trials. This in turn enables better drawing of accurate results from the conclusions reached. Furthermore, the data’s reliability will help in the formulation of hypothesis hence making it to generalize the results and apply them to all universities. Factors such as inaccurate data coding, fatigue of the interviewer and the students, interviewer’s bias or ambiguity of instructions to the students may influence reliability. However, application of measures like test-retest and internal consistency will counter reduced reliability. On the other hand, validity is the degree to which the study accurately reflects the specific concept the researcher is attempting to study. Since data validity is greatly influenced by the presence or absence of systematic error in the data, the research will incorporate systematic means of data collection as explained earlier. Lastly yet importantly, ethical standards will be keenly abided by in order to ensure the conduction and subsequent reporting of ethically conducted research. The research will ensure protection of the participants’ rights for instance, by ensuring informed consents and respecting their privacy. Ethical standards will be followed when enacting the research design as well as putting it into practice. Most importantly, the ethical standards will ensure the researchers safety throughout the research process aimed at deducing the factors influencing the depreciating performance of university students. References Creswell, J. W. (1997). Research design: Qualitative & quantitative approaches. Thousand Oaks, Calif: Sage Publications. Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, Calif: Sage Publ. Maxwell, J. A. (2007). Qualitative research design: An interactive approach. Thousand Oaks, Calif. Sage. Salkind, N. J. (2010). Encyclopedia of research design. Thousand Oaks, Calif: SAGE Publications. Read More