Quantitative research

Quantitative research Introduction (150) Quantitative research is the investigation of phenomena that give themselves to exact measurement and quantification, which attached a rigorous and controlled design (Polit Beck 2008). Its has main purpose is to measure concept or variables objectively in numerical and statistical process, also relationship between variables (Parahoo 2006). The research methods are obtained from research design and generally include sample, intervention (if applicable), instruments, data collection, and data analysis (eds. Joyce Meredith 2006). In many instances, the quantitative research needs such as questionnaires or interview, computers and large sample sizes. This article is to explore of a quantitative research by Kalij and William (2009) the view of its data objectivity, sample size and selection, process data collection, data presentation and analysis, using the relevant references to support this discussion. The objectives are to prove with evidences if the Khalij and Williams study (2009) fulfills the requirements of quantitative research. Objectivity (150) Objectivity is amount of involvement of the researcher relates during the collection and analysis of the data (Marcella n.d.a). Polit and Beck (2008) argued objectivity meant researcher tend to stay away any impression of subjectivity or impersonal during study process. Parahoo (2006) explained the data which collected and analyzed are expected to be free from bias between researcher and participants. It means the researcher is detached or does not active participate from practice and there is no intervention between researcher and participant. Also the participants use the same actual measurements for data collection. Further the bias of research can be minimized and the objectivity of the study can be maximized. In The Kalish and Williams study (2009) showed that they did not participate and used measurements to collect the data. It showed in their study that during data collection, they involved staff nurses in four hospitals and used psychometric testing tool. Hard data (148) Hard data is characteristic of quantitative study. Polit and Beck (2008) said that quantitative data are the information obtained during process of study course in a quantified (numeric value). (Janet Houser 2008) argued the other numerical data specific patients symptoms put in rank order the scales which contained intervals, comparisons between subjects. Other instance the researchers with rigorously designed tools should be able to grasp the reality (Parahoo 2006). For example scale of depression and pain in numeric value. That means the data can be measured and quantified in some way. In the nursing practice we always face with the hard data. For instance are physical (height, weight, gender), physiological (vital signs, laboratory results, visual acuity), past medical histories, psychological and social or behavior. This data are shown in Kalish and Williams study (2009) during their study while collected data: sample size, genders, experience, education degree, and work location in quantified data. Statistic (142) Statistic is very important aspect in the quantitative research. After data are collected can be analyzed using statistic and presented in numerical form. Polit and Beck (2008) said that statistic is an estimate of a parameter, calculated from sample data. They emphasized statistics are used to test hypotheses and evaluate the believability of the finding. Researchers usually use statistical computer to expedite calculation and ensure accuracy (ed. Joyce and Meredith 2006). They mentioned statistic methods are used in every process include in the final report to search the correlations, comparisons of means, trend and significance of finding to refute hypothesis. Its reinforce that statistic is one of characteristic in quantitative research. Its applied in Kalishs study process while selecting data sampling and analyzing data which showed in the tables and the data was analyzed by statistical computer, descriptive statistic and inferential statistic. Sample selection (269) Janet Holt (2009, p. 235) said that sampling is the process to select a small group of participant for study with the goal of making generalization from large population based on findings. Polit and Beck (2008) argued that sampling is the process of selecting portion of the population to represent from the entire population element. There are differentiation ways in sample selection; sampling designs, sample size and sampling steps. Polit and Beck (2008) mentioned that there are two sampling designs; probability sampling and non probability sampling. Probability sampling contents; simple random, stratified random, cluster and systemic sampling. The non probability sampling has contents; convenience, quota and purposive sampling. Janet Houser (2008) explained sample size in quantitative research to determine sample in adequacy is power. Its an analysis to indicate the large of sample which needed to adequately detect a difference in result variable. Polit and Beck (2008) suggested the steps in this research sampling as follow; the population identify, the specific eligibility criteria, the specific sampling plan and sample recruitment. They emphasized researcher during sample recruitment to gain the participant cooperation use means of courtesy, persistence, incentives, research benefits, sharing results, convenience, and endorsement. This study sample collection was implemented in the kalishstudy. They used random sampling to collect the participant data from four hospitals in different unit. The study used N symbol which designed for the total number, and n symbol is designed for number of subject. They took large sample size with total samples (N=1098). They applied ratio of sample size during sample collection. They gained the participant cooperation which provided incentives (jumbo-sized candy and bar a pizza party). Data collection (285) Parahoo (2006) said data collection is methods to measure the data sampling which utilized the instrument tools: questionnaires, observation schedule and other measuring tools. He emphasized that the methods should be predetermined, structured and standardized. Polit and Beck (2003) developed data collection plan include identifying data needs, selecting types of measures, selecting and developing instruments, pre testing the data collection package, and developing data collection forms and procedures. They stressed Important aspects should be considered while implementing the data collection plans are the selecting research personal and personal and the training data collector. Nancy and Susan (2007) explained data collection is the process of obtaining the subject and collecting the data for the research. They explained five tasks during data collection process: recruiting subjects, maintaining consistency, maintaining controls, protecting study integrity, and solving the problem. Structure, quantifiability, obstrusiveness, and objectivity are important elements when selecting data collection instruments (Marcella n.d.a). We have to understand and maintain the important dimension the data collection methods when applied in the data collection plan, and implementing. The obtained data should be accurate, valid, and meaningful to respond the questions. Kalish and William implemented the data collection process in their study. They applied the approaching methods to maintain the important dimensions in data collection such as a tool of missed nursing care and reason for missed care. The Data collection plan used to identify data needs for instance in the describing sample characteristic of participants (table 2.). They construct the tool in their survey to gain the quantify data. While implementing data collection, they selected research personal is staff nurse experts. The training data collector was done which distributed a copy of tool, informed consent form and a letter explaining the study. Data analysis and presentation (299) Joyce and Meredith (ed. 2006) said data analysis is a systematic method of examining data gathered for any research investigation to support hypothesis. This system implements in the data analysis process: the data analysis preparation, the sample description, reliability of measurement test, exploratory analysis conduction, exploratory analysis, confirmatory analysis and posthoc analysis conduction (Nancy Susan 2007). Parahoo (2006) stated analysis data can be obtained from measurement level, and then analyzed with statistical level. He mentioned two statistic levels those are descriptive statistic and inferential statistic. There are several statistical computer programs (SPSS, SAS, LISREL, EQS, etc) which help the researcher to calculate these test statistics and their sampling distribution (eds. Joyce Meredith 2006). Marcella (n.d.b) explained there are two types of inferential statistics are parametric (t-test, ANOVA, Multiple regressions) and non parametric (Chi-square, Rank Correlation, Mann-Whitney U, Kruskal-Wallis). Quantitative research result may be presented in the tables, charts and graphs (Michael, Patricia Frances, 2007, cited Russell, 2005). In conclusion that data analysis is systemic method of examination data started from data collection which used measurement level then data were analyzed by statistical level and presented in the tables, charts and graphs to support hypothesis. Kalish and Williams study (2009) implemented the data analysis process which utilized measurement level and statistical level which completed using SPSS in their study. First step is checking of data accuracy using ordinal scale. The second is describing sample, they use central tendency and dispersion to test the contrast validity. The third is testing the reliability of measurement, they applied cronbach alpha coefficient. Fourth they conducted exploratory analysis as the extraction technique and varimax as orthogonal rotation method, also analyzed using oblique rotation. Fifth they conducted the confirmatory analysis used AMOS version 16. Last step they used analysis of variance (ANOVA) to conduct posthoc analysis. Result study was presented in the table presentation. Conclusion (118) This essay has tried to explore the Kalish and Williams study (2009) about the development and psychometric testing of a tool to measure missed nursing care. Their study has approached and fulfilled the major requirement of quantitative research characteristics that include objectivity, hard data and statistic. They applied quantitative study process which consists of conceptual, design and planning, empirical, analytic and dissemination phase as suggested by Polit and Beck (2004). They also utilized data analysis process of Nancy and Susan (2008). In general view of Kalish and Williams study (2009) has applied the quantitative research systematically. Improvement progress monitoring was done, but effectiveness overview the tool is still recommended to apply in the base practice for long period. References: 1. Carol L. Macnee Susan Mc. Cabe (2008) Understanding nursing research: using research in evidence-base practice, 2nd ed. Philadelphia: Lippincott and William a Wolter Kluwer Business. 2. Denise F, Polit Cheryl Tatano Beck (2010) Essentials of nursing research: appraising evidence for nursing practice, 7th ed. Philadelphia: Lippincot William Wilkins. 3. Denise F, Polit Cheryl Tatano Beck (2008) Nursing research: generating and assessing evidence for nursing practice, 8th ed. Philadelphia: Lippincot William Wilkins. 4. Denise F, Polit Cheryl Tatano Beck (2003) Nursing research: principle and methods, 7th ed. Philadelphia: Lippincot William Wilkins. 5. Kader Parahoo (2006) Nursing research principles, process and issues, 2nd ed. Hamspire: Palgrave Macmillan. 6. Janet Holt (2009) Reading research series quantitative research: an overview, British Journal of Cardiac Nursing, Vol. 4(5), pp. 234-236. 7. Janet Houser (2008) Nursing research: reading, using and creating evidence, Jones and Bartlett Publisher, Sudbury. 8. Joyce J.F. Meredith W. (eds) 2006, Encyclopedia of nursing, 2nd ed. New York: Springer Publishing. 9. Marcella Hart (n.d.a) Birthing a research project: data collection, International Journal of Childbirth Education, Vol. 22(3) pp. 27-31. 10. Marcella Hart (n.d.b) Birthing a research project: data analysis, International Journal of Childbirth Education, Vol. 22(4), pp. 24-28. 11. Michael Coughlan, Patricia Cronin Frances Ryan (2007) Step-by-step guide to critiquing research. part 1: quantitative research, British Journal of Nursing, Vol. 16(11), pp. 658-663. 12. Nancy Burns Susan K. Grove (2007) Understanding nursing research: building an evidence-base practice, 4th ed. Missouri, Saunders an Imprint of Elsevier.

Unit 74

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Kaplan Scavenger Hunt Paper Analysis - 1482 Words

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Combatting the Issue of Human Trafficking - 1294 Words

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