Evaluation of Data - Assignment Example

Methods of Data Collection Use of structured questionnaires and interview schedules will be the major methods of data collection (Stawarski, 2008). The researcher will contact the sampling units of the selected sample by administering questionnaires. The questionnaires will administered by the researcher with the help of two trained enumerators. Questionnaires will be useful since they are very appropriate to obtain factual information; it is free from bias as respondents answer the questions alone, with greater confidence.

It also ensures adequate time for respondents to give well thought out answers. Interview schedules will be used and will complement the questionnaire to phrase and rephrase questions to ensure that the needed information is obtained. In addition, it stimulates the respondents to think and answer the questions appropriately.Data Measurement and Statistical Analysis The processing of data prepares raw data for statistical analysis and presentation. Kothari, (1988) explains that this stage is appropriate for a scientific study and for making sure that all the relevant data is present for making comparisons and analysis.

The checking of questionnaires, coding of responses and statistical analysis will be done. Data computation will be done through Statistical Package for Social Sciences (Morgan, 2011). Descriptive data analysis will be used to show the characteristics of each variable under study. Regression analysis will be used to identify the presence of certain relations among variables such as the adoption a new technologies in hospitals and the reduction of medication errors. Descriptive statistics such as mean, frequencies and percentages will be evaluated using SPSS so as to bring out a clear picture about the study.

Expected Outcome The research is expected to prove that new innovations can actually reduce cases of medication errors in hospitals. The main development that is expected to reduce medication errors is the bar code method of administering medication. The research will focus on the different innovations that help reduce medication errors. The interviewed nursing students are expected to point out reasons for their medication errors, if any, such as inadequate training and wrong patient details (Lan et al, 2014).

They will identify new innovations in hospitals as the reason for reduced medication errors. Implementing the Identified Changes The research will identify areas which need immediate attention. The research will point out several recommendations to the university and the hospitals where the nursing students were placed. The research will identify problems which lead to medication errors and which need to be solved in order to avoid unnecessary loss of life or injuries by the patient. If the problem is with the particular hospital, the researcher will petition the hospital management to reevaluate the area where the problem exists.

If the problem is with the university, the researcher will petition the nursing department to consider the recommendations. Evaluation Method The Logit model will be used to evaluate the data received. The focus of the study is to evaluate whether medical errors can be avoided using new technology and techniques such as the bar code medication administration (BCMA) method of administering medication. The model is specified as; Log P(Y) = α + ∑βiXi + μi =Pi/1-PiY= (0 or 1) no medical errors=1, medical errors=0α = Constant termβi’s = Logistic coefficients for the independent variablesμi = Error termXi’s = Independent variablesThe calculation will be done using a 5% level of significance.

ReferencesKothari, C.R (1988): Research Methodology. New Delhi: Vrinda Publications (P) Ltd.Lan, M., Zhu, L., & Zhou, Q. (2014). Medication administration errors made by nurses reflect the level of pharmacy administration and hospital information infrastructure. Journal Of Clinical Nursing, (5-6), 894.Morgan, G, Leech, N, Gloeckner, G. and Barrett, K (2011). IBM SPSS for Introductory Statistics: Use and Interpretation, 4th Edition. United Kingdom: Taylor and Francis. Stawarski, C and Phillips, P. (2008). Data Collection: Planning for and Collecting All Types of Data.

New Jersey: John Wiley and Sons.