Statistical Principles Of Research Design And Analysis PdfBy Eleasar M. In and pdf 24.05.2021 at 11:10 6 min read
File Name: statistical principles of research design and analysis .zip
While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase Savenye, Robinson,
- Research Design and Methodology
- Understanding Statistics and Experimental Design
- Statistical principles for veterinary clinical trials
Research Design and Methodology
While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase Savenye, Robinson, The form of the analysis is determined by the specific qualitative approach taken field study, ethnography content analysis, oral history, biography, unobtrusive research and the form of the data field notes, documents, audiotape, videotape.
An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers Shepard, , and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well. These include:. Having necessary skills to analyze A tacit assumption of investigators is that they have received training sufficient to demonstrate a high standard of research practice.
A number of studies suggest this may be the case more often than believed Nowak, ; Silverman, Manson, Indeed, a single course in biostatistics is the most that is usually offered Christopher Williams, cited in Nowak, Ideally, investigators should have substantially more than a basic understanding of the rationale for selecting one method of analysis over another.
This can allow investigators to better supervise staff who conduct the data analyses process and make informed decisions Concurrently selecting data collection methods and appropriate analysis While methods of analysis may differ by scientific discipline, the optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought.
Drawing unbiased inference The chief aim of analysis is to distinguish between an event occurring as either reflecting a true effect versus a false one. Any bias occurring in the collection of the data, or selection of method of analysis, will increase the likelihood of drawing a biased inference. Bias can occur when recruitment of study participants falls below minimum number required to demonstrate statistical power or failure to maintain a sufficient follow-up period needed to demonstrate an effect Altman, Inappropriate subgroup analysis When failing to demonstrate statistically different levels between treatment groups, investigators may resort to breaking down the analysis to smaller and smaller subgroups in order to find a difference.
Although this practice may not inherently be unethical, these analyses should be proposed before beginning the study even if the intent is exploratory in nature. If it the study is exploratory in nature, the investigator should make this explicit so that readers understand that the research is more of a hunting expedition rather than being primarily theory driven. Although a researcher may not have a theory-based hypothesis for testing relationships between previously untested variables, a theory will have to be developed to explain an unanticipated finding.
Indeed, in exploratory science, there are no a priori hypotheses therefore there are no hypothetical tests. Although theories can often drive the processes used in the investigation of qualitative studies, many times patterns of behavior or occurrences derived from analyzed data can result in developing new theoretical frameworks rather than determined a priori Savenye, Robinson, It is conceivable that multiple statistical tests could yield a significant finding by chance alone rather than reflecting a true effect.
Integrity is compromised if the investigator only reports tests with significant findings, and neglects to mention a large number of tests failing to reach significance. While access to computer-based statistical packages can facilitate application of increasingly complex analytic procedures, inappropriate uses of these packages can result in abuses as well.
Following acceptable norms for disciplines Every field of study has developed its accepted practices for data analysis. Resnik states that it is prudent for investigators to follow these accepted norms. If one uses unconventional norms, it is crucial to clearly state this is being done, and to show how this new and possibly unaccepted method of analysis is being used, as well as how it differs from other more traditional methods. For example, Schroder, Carey, and Vanable juxtapose their identification of new and powerful data analytic solutions developed to count data in the area of HIV contraction risk with a discussion of the limitations of commonly applied methods.
Determining significance While the conventional practice is to establish a standard of acceptability for statistical significance, with certain disciplines, it may also be appropriate to discuss whether attaining statistical significance has a true practical meaning, i.
Thompson and Noferi suggest that readers of counseling literature should expect authors to report either practical or clinical significance indices, or both, within their research reports. Lack of clearly defined and objective outcome measurements No amount of statistical analysis, regardless of the level of the sophistication, will correct poorly defined objective outcome measurements.
Whether done unintentionally or by design, this practice increases the likelihood of clouding the interpretation of findings, thus potentially misleading readers. Provide honest and accurate analysis The basis for this issue is the urgency of reducing the likelihood of statistical error. Common challenges include the exclusion of outliers , filling in missing data, altering or otherwise changing data, data mining, and developing graphical representations of the data Shamoo, Resnik, Manner of presenting data At times investigators may enhance the impression of a significant finding by determining how to present derived data as opposed to data in its raw form , which portion of the data is shown, why, how and to whom Shamoo, Resnik, Nowak notes that even experts do not agree in distinguishing between analyzing and massaging data.
Shamoo recommends that investigators maintain a sufficient and accurate paper trail of how data was manipulated for future review. Data recording method Analyses could also be influenced by the method in which data was recorded.
For example, research events could be documented by:. While each methodology employed has rationale and advantages, issues of objectivity and subjectivity may be raised when data is analyzed.
Training of Staff conducting analyses A major challenge to data integrity could occur with the unmonitored supervision of inductive techniques. Content analysis requires raters to assign topics to text material comments. The threat to integrity may arise when raters have received inconsistent training, or may have received previous training experience s.
Previous experience may affect how raters perceive the material or even perceive the nature of the analyses to be conducted. Thus one rater could assign topics or codes to material that is significantly different from another rater. Strategies to address this would include clearly stating a list of analyses procedures in the protocol manual, consistent training, and routine monitoring of raters.
Reliability and Validity Researchers performing analysis on either quantitative or qualitative analyses should be aware of challenges to reliability and validity. For example, in the area of content analysis, Gottschalk identifies three factors that can affect the reliability of analyzed data:. The potential for compromising data integrity arises when researchers cannot consistently demonstrate stability, reproducibility, or accuracy of data analysis. Extent of analysis Upon coding text material for content analysis, raters must classify each code into an appropriate category of a cross-reference matrix.
Relying on computer software to determine a frequency or word count can lead to inaccuracies. Further analyses might be appropriate to discover the dimensionality of the data set or identity new meaningful underlying variables. Whether statistical or non-statistical methods of analyses are used, researchers should be aware of the potential for compromising data integrity.
While statistical analysis is typically performed on quantitative data, there are numerous analytic procedures specifically designed for qualitative material including content, thematic, and ethnographic analysis.
Regardless of whether one studies quantitative or qualitative phenomena, researchers use a variety of tools to analyze data in order to test hypotheses, discern patterns of behavior, and ultimately answer research questions.
Failure to understand or acknowledge data analysis issues presented can compromise data integrity. Gottschalk, L. Content analysis of verbal behavior: New findings and clinical applications.
Jeans, M. Clinical significance of research: A growing concern. Canadian Journal of Nursing Research, 24, Lefort, S. The statistical versus clinical significance debate. Image, 25, Kendall, P. Normative comparisons in therapy outcome.
Behavioral Assessment, 10, Nowak, R. Problems in clinical trials go far beyond misconduct. Resnik, D. Statistics, ethics, and research: an agenda for educations and reform.
Accountability in Research. Schroder, K. Methodological challenges in research on sexual risk behavior: I. Item content, scaling, and data analytic options. Ann Behav Med, 26 2 : Shamoo, A. Responsible Conduct of Research. Oxford University Press.
Principles of Research Data Audit. Gordon and Breach, New York. Shepard, R. Ethics in exercise science research. Sports Med, 32 3 : Silverman, S. Research on teaching in physical education doctoral dissertations: a detailed investigation of focus, method, and analysis. Journal of Teaching in Physical Education, 22 3 : Smeeton, N. Conducting and presenting social work research: some basic statistical considerations.
Br J Soc Work, Thompson, B. Statistical, practical, clinical: How many types of significance should be considered in counseling research? For example, research events could be documented by: a.
References: Gottschalk, L. Top ——————————————————————————————————————.
Understanding Statistics and Experimental Design
There are a number of approaches used in this research method design. The purpose of this chapter is to design the methodology of the research approach through mixed types of research techniques. The research approach also supports the researcher on how to come across the research result findings. In this chapter, the general design of the research and the methods used for data collection are explained in detail. It includes three main parts. The first part gives a highlight about the dissertation design.
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.
Statistical principles for veterinary clinical trials
If your institution subscribes to this resource, and you don't have a MyAccess Profile, please contact your library's reference desk for information on how to gain access to this resource from off-campus. Forgot Username? About MyAccess If your institution subscribes to this resource, and you don't have a MyAccess Profile, please contact your library's reference desk for information on how to gain access to this resource from off-campus. Learn More.
RDR addresses the basic question, "Is it good. Highlight the reasons why researchers employ a mixed methods design e. Evaluation Tool for Quantitative Research Studies Building on work within a project exploring the feasibility of undertaking systematic reviews of research literature on effectiveness and outcomes in social care, a set of evaluation tools have been developed to assist in the critical appraisal of research studies. Quantitative analysis QA is a technique uses mathematical and statistical modeling, measurement, and research that to understand behavior.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Kuehl Published Mathematics.
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.
Skip to main content Skip to table of contents. Advertisement Hide.