Strengths and Weaknesses of Research Designs

When conducting research, a given research strategy must be chosen with the ultimate goal in mind to solve a question or problem that pertains to management. There are different strengths and weaknesses with different research designs and this paper focuses on three research designs, providing a brief analysis and explanation of the benefits and limitations imposed by each. Developed strategies will include: surveys, laboratory experiments and field studies. In addition, four common measurement scales used in research today will be discussed, with real examples provided for each of the measurement scales.

The questionnaire uses questionnaires and can be used to describe or predict phenomena based on their findings (Davis, 2005, p.146). A survey is a type of ex post facto design, which is a study designed to determine what pre-existing causal conditions exist between groups (University of Port Elizabeth, n.d.). Surveys require interaction with respondents and are probably the most used research design today (Davis, 2005, p.146). Surveys are useful because of their capacity to target large populations more effectively than field studies and are adaptable to any research need (Davis, 2005, p.146). Furthermore, REACT (2000) explains that surveys can be repeated in the future to assess any changes that have taken place. But he also examines his own faults. Davis (2005) surveys do not allow the researcher to intervene in an attempt to control independent variables, but rather according to the level of concepts that are focused on in the study, so the survey will not be useful if the researcher needs it. controlled independent variable (p.146). In addition, REACT (2000) expands on the weaknesses of surveys by explaining that they often require special skills from the researcher in sampling, proper questioning, design, and analysis. Furthermore, REACT (2000) explains that surveys do not always reveal clear answers to questions about where the underlying factor or cause lies.

However, contemplation can still be useful, and because of their cheapness and wide applicability in many different situations; so contemplation is not to be overlooked when he devises an investigation.

Laboratory experiments are “done in an artificial setting where the researcher intervenes and controls some independent variables in a highly controlled manner” (Davis, 2005, p. 148). Because laboratory experiments are highly structured, they have the greatest chance against design error and are the most accurate models available in the research arena (Davis, 2005, p.148). The variable the researcher manipulates is the independent variable, while the variable to be measured is the dependent variable (Maricopa Community Colleges, n.d.). Laboratory experiments allow the researcher to see cause and effect relationships more clearly than other research methods since they can change the independent variable(s) , holding all other conditions that would affect the variable constant (Maricopa Community College, n.d.). But laboratory experiments are also subject to their limitations. Davis (2005) explains: An artificial research setting can cause changes in dependent variables that cannot occur in a real-world setting (p.149). Maricopa Community College (n.d.) elaborates on the limitations of the laboratory experiment, explaining that researchers can only evaluate real-world ethical and practical research. Davis (2005) elaborates by explaining that artistic experience can change their behavior in their study (p. 149). Although there are limitations with the technical environment and possible behavior changes of the research participants, laboratory experiments are still the most advanced technique to allow the researcher maximum control over his research.

A field study is a type of post facto design that combines literature research, observational experiences, and single or multiple case studies that allow researchers to try to identify important variables and their relationships (Davis, 2005, p.144). . Field studies are considered ex post facto designs because no manipulation or control is performed and data is collected in the most nonintrusive way possible (Davis, 2005, p.144). Maricopa Community College (n.d.) explains that field studies are useful “when researchers want to get a detailed contextual picture of someone’s life or a phenomenon.” In addition, field studies are an option for the researcher when conducting a laboratory experiment is considered impractical or unethical (Maricopa Community College, n.d.). The weaknesses of field studies include the lack of control by the researcher, since the experiment is done after the fact and the difficulty of obtaining all useful information due to noise and other interruptions in the study setting (Davis, 2005, p.146). As Maricopa Community College (n.d.) explains, behavior can only be described, not explained with a field study. In addition, field studies often involve small numbers of participants, so it is difficult to make generalizations about entire populations or large groups based on findings (Maricopa Community College, n.d.). Despite the disadvantages of designing field studies, they allow researchers to gain insight into the natural environment and these discoveries can be made. compiled with findings from other research studies to gain a broader understanding of the phenomena studied.

The four measurement scales described are nominal, ordinal, interval, and proportion scales. Nominal measurement is used in the classification of objects, people, or groups (Davis, 2005, p. 180). Nominal measurement scales allow division to be done by dividing equalities and inequalities (Davis, 2005, p. 181). An example of a nominal measurement would be using 1 to indicate power on and 0 to indicate power off. Ordinary measurement allows the elements to be arranged in order; however, the distances of the elements do not mean anything (Trochim, 2002). An example of an ordinal measurement would be to create a survey to identify income levels in a survey. It would look like this: 0= less than $20000; 1=$20,001-$40,000; 2=$40,001-$60,000; 3=$60,001-$80,000; 4=$80,001-$99,999; 5=$100,000 or more. Interval scales have the properties of nominal and ordinal scales, and having points on the measurement scale (Davis, 2005, p. 183). As Trochim (2002) explains, attributes with a distance between them have meaning and the average of the interval variables can be calculated. When you take the income situation to illustrate the measurement interval, you have this example: Assume persons A and B have income< /a> between $55,000 and $100,000 respectively. Person A can determine using intervals that Person B makes $45,000 more than him. This is calculated by subtracting $100,000 from $55,000. In addition, the average income of A and B can be calculated by adding both figures together ($155,000) and dividing by two, to come up with an average income of $77,500. The system of measurements preserves all the attributes of the previous three measurement scales, with the addition of an empirical part that is not absolute (Davis, 2005, p.184). Denoting the concept, Trochim (2002) explains that one can make a fraction (that is: a ratio) using a measurement technique. For example, a store customer may count purchases in January and July of a given year. Let’s assume that January’s customer count is 25,000 because the store just opened and July’s customer count is 50,000. The supply could have been expanded from January to February in customers from January through July 2/1. This is possible because the supply chain uses zero and zero absolutes as the basis for generating their accounting policies.