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Please read the lecture and respond to the discussion questions in APA format with reference
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Measurement, Statistics, and Appraisal
The final components of the research process are data collection/measurement, data analysis and interpretation of the results.
Measurement is a process for assigning numbers to objects, events, or situations in a consistent manner. Measurement must begin by clarifying what is to be measured. Each variable in a quantitative study must be able to be reduced to a number.
Instrumentation is a part of measurement. Instruments are developed according to specific standards to examine specific variables. The purpose of instrumentation is to produce evidence that is trustworthy in evaluating research outcomes. Instruments need to be reliable and valid. Reliability refers to consistency or precision, whereas validity indicates that the instrument actually measures what it is supposed to measure.
Strategies must be developed to measure the object, element, or characteristic being studied. Direct measurement is simply measuring concrete factors, such as height and weight. Indirect measurement measures attributes that represent a concept. When the object to be measured is abstract, a conceptual definition is necessary to clarify what is being measured and to select appropriate means of measurement (Cooper & Schindler, 2003).
There will always be errors in measurement, because there is no perfect measure. The difference between what exists and what is measured by the research tool is called measurement error, and is found in both direct and indirect measures. Two types of measurement error are random and systematic.
Random error is a fluctuation in the statistics in measured data due to limitations of the measurement device. This type of error usually results from the researcher’s inability to make the same measurement exactly the same way to get exactly the same results. Factors that can result in random error include transient personal factors, such as hunger and motivation; situational factors, such as distraction and environment; variations in administration of the measurement, such as the use of different wording in interviewing; and processing of data, such as recording errors.
The systematic error occurs when a problem persists throughout the research study and inaccuracies are reproduced. Some systematic error will occur in almost every measure. Therefore, closely linking the abstract theoretical concept with the instrument, and refining the instrument, will greatly decrease systematic error. The use of more than one measure of a concept will also decrease systematic error (Simon & Francis, 2001).
Levels of Measurement
Data generated by an instrument may be at nominal, ordinal, interval, or ratio level of measurement, depending on the instrument. Nominal is the lower end of the scale, and ratio is the higher end. Nominal-scale measurement is used when data can be organized into categories, but the categories cannot be compared. Examples of nominal data are gender and race. Numbers are assigned to nominal categories as labels, not for mathematical calculations.
Limitations and Assumptions
Limitations are restrictions that may decrease the generalization ability, extension of the study sample to a larger population, or the findings of the study. Two types of limitations are conceptual and methodological. Conceptual limitations stem from the limits within the theoretical framework due to unclear concepts or relationships and limit the abstract generalization of the study findings. They are reflected in the frame of reference, conceptual definitions, and operational definitions. The methodological limitations limit the credibility of the findings and restrict the population to which the research findings can be applied. These limitations result from factors such as weak design and inaccurate use of statistical analysis.
Data collection is the gathering of relevant information in a precise, systematic manner. Data collected for quantitative studies are numerical. Numerous methods exist for data collection including observation, scales, and questionnaires.
Data analysis is the process of applying statistical techniques systematically to describe data. Data analysis is conducted to organize, reduce, and give meaning to data.
The level of measurement is associated with the type of statistical analysis. Therefore, the highest possible level of measurement should be used. Data can be analyzed by hand, by using computers, or by statisticians. Data analysis techniques depend on the research design, researcher expertise, and type of data collected (Brink & Wood, 1998).
Data collection and data analysis are action-oriented, and when they are completed, activity moves to abstract thinking through introspection and reasoning to synthesize and evaluate the entire research process, organize the meaning of the results, and forecast the usefulness of the findings. During this process, the researcher forms conclusions from the data, implications for further research, and implications for nursing practice (Brink & Wood, 1998).
Qualitative Data Collection Methods
Qualitative research requires a simple data collection method because the complexity of the research is in the data itself. If the researcher is too complex in his or her method, the reaction between the complex data and the complex method could be disastrous. Common data collection methods include observation, interviewing, and examining historical documents or written texts. A qualitative inquiry or interview should include understandable, and rather simple, questions.
Qualitative research should start with asking simple questions that evolve into complex answers. Using qualitative interviews allows the researcher to gather detailed, rich information. When doing interviews, it is necessary to reach the saturation level, which means the information is repeated. Robson (2002) offers some helpful advice when doing an interview for a qualitative study. The interviewer should encourage the participants to talk freely. This can be encouraged in the following ways:
Interviewers must listen more often than they speak, and should not interject their personal opinions.
Questions should be delivered in a clear, concise, and nonthreatening way. Interviewers should not put participants on the defensive nor try to confuse them.
The researcher should not lead the participant in answering the questions a certain way or in a way the researcher wants the questions answered.
Unlike quantitative data analysis, qualitative data analysis actually begins when the researcher is still collecting data. The researcher will usually gather large amounts of textual data in some fashion. There are three stages of data analysis:
·Descriptive: The researcher becomes immersed in the data, and finds it necessary to read and reread the information collected. If the researcher misses anything during this period of time, important information may be lost.
·Analysis: During this phase, themes and patterns from the data collected are identified. These themes and patterns are relationships that the researcher feels are present, based on what the participant has said.
·Interpretation: The researcher offers his or her explanation on what is happening with the study. Researchers develop tentative propositions and form them into a tentative theory. The validity of the theory is then tested by predictions, which means the theory must be tested with a similar or same-sample population.
Qualitative data may be hard to assemble and organize because of the voluminous amount of information. One challenge of qualitative research is data reduction because everything may look more important than it is. Thousands of pages must be reduced to a short report. Bogdan and Biklen (1998) suggest that “the process of data analysis is like a funnel: Things are open at the beginning (or top) and more directed and specific at the bottom” (p. 7).
There are certain strategies for data presentation:
·Natural, in a way that resembles the phenomenon being studied in a sequential order
·From most simple to most complex
·According to themes or patterns
·By the researcher’s theory or theories regarding the phenomenon
·Arranged for storytelling
·From the most important to the least important (major to minor)
·Dynamic, where the researcher will save discoveries or surprises for the end of the study
Understanding the components of quantitative studies is critical to advance nursing knowledge and improve patient outcomes. By mastering quantitative research, nurses will be able to implement change and improve the care that they provide to patients, families, and communities.
Qualitative research is an investigative process in which the researcher attempts to make sense of a social phenomenon by replicating, cataloguing, contrasting, or classifying the object of the study. Therefore, the researcher is entering the participants’ world by trying to understand their perspectives and meanings.
Bogdan, R. C., & Biklen, S. K. (1998). Qualitative research for education: An introduction to theory and methods. Boston: Allyn & Bacon.
Brink, P. J., & Wood, M. J. (1998). Advanced design in nursing research (2nd ed.). Thousand Oaks, CA: Sage.
Cooper, D. R., & Schindler, P. S. (2003). Business research methods (8th ed.). Boston: McGraw-Hill Irwin.
Robson, C. (2002). Real world research: A resource for social scientists and practitioner-researchers (2nd ed.). Oxford, United Kingdom: Blackwell Publishing.
Simon, M. K., & Francis, J. B. (2001). The dissertation and research cookbook (3rd ed.). Dubuque, IA: Kendall/Hunt.
Extraneous variables may have an influence on the dependent variable. In what ways do researchers attempt to control extraneous variables? Support your answer with current literature.
Describe the levels of evidence and provide an example of the type of practice change that could result from each.