Research Methods and Ethnography Design of Research

Decision-making occurs in everyday life. Several factors exist that may influence the decision-making process, including drugs and substance abuse, brain injuries, neurological disorders, and genetics. Although various factors influence the decision-making process, it is important to observe specific factors. Thus, the question arises: how do neurological factors influence the decision-making process? It is suspected that drug abuse significantly impacts the decision-making process because it changes the chemicals in the brain. It is important to research various methodologies that provide a better understanding of how this topic can be studied and how to test the hypothesis.

Thus, correlational, ethnography, posttest-only, and parallel designs were chosen and are discussed further.

In the first peer-reviewed journal article, the non-experimental choice was related to correlational. Rutter (2011) discussed how studies could be performed that produce results based on correlational data. Naturally occurring experiences can be studied and compared to produce results and lead to a better understanding. Conducting a correlational study often results in realizing that not one factor, but several, may be the cause. Identifying the cause can be complex. The researcher must identify and compare what truly happened during the experience while analyzing what could potentially have happened if the experience had not occurred. There are five factors to consider when conducting correlational studies including: genetics, selection, reverse causes, misidentifying the risk involved, and the effects of variables reflecting the cause (Rutter, 2011). The author provided various examples of naturally occurring experiments. As with any study, there are strengths and limitations. Natural experiments appear to be most effective when identifying probable cause-effect relationships (Rutter, 2011). This design is useful when researching possible outcomes as well as personal factors.

Conducting a correlational study on neurological factors that influence the decision-making process would be helpful, as it would provide a better understanding of the factors related to the topic. Studying natural experiences and the outcomes of their decisions would help advance research. With correlational experiments often resulting in several factors that may be the cause, they can be used to identify neurological factors related to the process of decision-making.

Emerson (2010) discusses a qualitative research method using the ethnography design. Ethnography is a design that studies the social worlds of individuals through fieldwork observations. This design takes the information that is known and aims to study the topic and provide additional information. Although this design may be helpful, researchers must be cautious not to take the known information and misinterpret the meaning. While conducting fieldwork observations, there may be concerns amongst researchers and the study. It may be difficult for the researchers to understand the meaning of the members and to connect the members with various categories. In addition, applying this information within various settings may be difficult to process. Finally, interpreting the behavior may be challenging as what really happened may be seen or speculated differently. The researchers are typically outside observers attempting to interpret data. Thus, it is critical for the researcher to focus on the pathway of the study while producing data from both the researcher and the topic. The author provides an example of where errors can occur in this misinterpretation of data.

The ethnographic design of research would be useful when studying neurological factors of decision-making. For example, it is already understood that various factors impact the decision-making process. Using the ethnographic design would assist in digging deeper into the topic and provide a better understanding of the influence these factors have. It is imperative when conducting the study to refrain from making assumptions and misinterpreting data.

Prior research has indicated a validity concern between pretest and posttest results. However, recent studies have shown that these tests can be useful when measuring various data. Williams & Zimmerman (2011) analyzed the classical test theory (CTT) and discovered the reliability of difference scores. The CTT has two equations which interpret test scores. Although some researchers have contended that the two equations may produce errors, according to Williams & Zimmerman (2011), this is caused because the researchers have not based their conclusions on the second equation. The validity of this assumption appears to be correct when the pretest and posttest are parallel. However, if the tests are not parallel, using the equations to interpret gain scores will appear to be unreliable. Measurements of data are typically done numerically. True and observed scores are expected to change after an intervention has been implemented. Researchers may have assumed that these equations are unreliable due to the fact that misinterpreting assumptions can impact the calculations of test scores. For example, conversion of test scores and non-parallel tests can impact the results. Thus, using these equations has been effective and reliable to interpret gain scores when scores are input correctly, when there are score conversions, and when tests are parallel. Various examples were used for the equations in order to provide a better understanding of test reliability.

Using pretest and posttest results can be useful when conducting research on neurological factors that influence decision-making. The tests must be parallel and measurable. In addition, the equations used to measure gain scores can be useful when assessing results of data. For example, a pretest could be given, followed by an intervention, and then finally a posttest to measure the changes.

Finally, Hayton (2011) provides guidance on conducting parallel analysis design. Several factor retention methods exist, but this article offers a rationale for using the parallel analysis design. Parallel analysis can be executed in various ways. In the guidelines, Hayton (2011) outlines the SPSS protocol, which includes four steps and is the most commonly followed pattern of parallel design. In the first step, data is collected randomly and mirrors the same number of variables and observations as the data being assessed. In the second step, error-free measurement data should be used as eigenvalues are extracted. The same number of variables should be used in each data set. Steps one and two should be repeated several times. According to Hayton (2011), it is desirable to repeat these steps as many as 50 times or more. In step three, an average of the scores amongst the 50 sets is calculated. In the fourth and final step, the actual data is compared with the random data. According to Hayton (2011), the parallel design provides accurate results in determining which factors should be retained. The repetition of data can be quite time-consuming while conducting the study. The concern for overfactoring typically occurs in small samples rather than large samples. Overall, the use of parallel analysis proves beneficial when conducting factor retention approaches.

Using the parallel analysis approach would be helpful when conducting research. The randomization of participants could be selected and then observed while performing various tasks related to decision making. Determining the factors that cause the decisions can be evaluated. Thus, the retention of factors related to decision making can be evaluated.

Part of the research design involves worldviews that are related to the methodology and experimental strategies used. The four worldviews include postpositive, constructivism, advocacy or participatory, and pragmatic. In addition to selecting a method of study such as qualitative, quantitative, mixed-method, or non-experimental, the researcher also selects a specific type of study. During a non-experimental correlation study, observing and comparing data is related to the participatory worldview. Through natural experiences, measurements of these observations are made within the world based on individual experiences. Thus, the information leads to a better understanding. A qualitative study using an ethnography design would be related to a constructivist worldview. This viewpoint allows the researcher to observe behavior and draw conclusions over a period of time based on the viewpoints of participants. Quantitative studies, which include post-test only, relate to the postpositive worldview. During the research, data is collected to support or disprove the hypothesis. The testing of the hypothesis is performed, as well as following a strategic plan. During a mixed-methods approach, both qualitative and quantitative methods are used. The mixed-methods approach relates to a pragmatic worldview. Collecting various data contributes to a better understanding of the study.

Within these various approaches, the one that most closely relates to my worldview would be postpositivist inquiring about attitudes. The idea of using pretest and posttest measurements is critical in my professional viewpoint. It provides an understanding of attitudes prior to intervention and then a measurement after intervention, thus leading to a specific understanding directly related to the hypothesis. This relationship either supports or refutes the hypothesis based on the results of the experiment.

Did you like this example?

271

14