The quantitative approaches to research fill a continuum where one side has designs that only observe variables and describes connections. On the other hand, research designs include close control of variables with the clear establishment of relationships. This paper looks at four types of quantitative research methods with an example study for each.
Experimental
The researcher is deliberately manipulating and controlling the independent variable. The aim is to observe the effect of the change on a dependent variable. The aim is to establish the cause-effect relationship among a group of variables, and this is often conducted like a laboratory study, but may not have to use laboratory settings. The Independent variable is manipulated to determine effects on the dependent variables, and this is on subjects that are randomly assigned to the treatments in the experiment, they are not identified in natural groups as they occur.
The study Mago (2014) was a classroom experiment meant to show how both fiscal stimuli cause a greater aggregate output and the fiscal stimulus spending that was most effective at addressing GDP impact. The two stimuli are lowering taxes and raising government expenditure in an economy. The study randomly picks six students and has then in front of a class where they role-play as business owners. In the experiment, the instructor acts as the government that is bringing a stimulus plan to the economy. Besides, the other students in the classroom serve as consumers.
The study manipulates assumptions about the experiment concerning payments, taxes and other economic parameters that affect the results. In analyzing data, the study looks at consumption effects and GDP changes before and after the respective changes in the stimulus plan. The stimulus is the independent variable while the consumption effects and GDP are the dependent variables. Data is collected by observation and recording of transactions. The findings are that money generated by tax cuts or fiscal interventions goes to savings and not an expenditure, which negatively affects GDP growth. The study can be used in other classroom settings, and the findings can only help to inform the relationship of stimulus by the government. However, they cannot predict with certainty real-life happenings.
Quasi-Experimental
It is similar to experimental design, only that the researcher can only measure the independent variable and not manipulate it. Researchers used naturally formed groups, without the option of assigning groups at random. The study compares groups exposed to treatment and those not exposed.
Arthur and Hardy (2014), sought to report a field-based quasi-experimental study that looks at the effectiveness of transformational leadership. They reviewed leadership intervention. The focus was on remediating poor performance. In this regard, the focus was on organization elements that were low performing. This was according to the perception of senior management. They selected an infantry recruit training establishment in the UK for their sample based on its mandate of transforming civilian recruits into capable soldiers.
Data was collected four months before the intervention, and then another one occurred eight months after the intervention. The study collects data on follower perceptions. This is about their leader’s behavior. Its focus was on group cohesion and training outcome data. Dependent variables are the perceptions of leadership and desirable behaviors while the intervention is the independent variable. The interventions include training through a series of workshops and coaching sessions. Participants were encouraged to take ownership of the project during the intervention.
The study shows that perceptions of leadership. Other observed elements are group cohesion and training outcomes. They support a positive view of the intervention. It looked at beneficial effects. These were considered as desirable behaviors. They increased in the experimental group. Also, desirable behaviors remained the same in the experimental group while decreasing in the control group.
Generalizability of findings
Overall, the findings are generalizable due to sufficient explanation and measurement of variables and the use of a conventional organizational setting.
Ex-Post Facto
The study design investigates cause and effect relationships by looking at the existing conditions and searching for causal factors from the past. The researchers pick the effect or the dependent variable. There is little or no control of the independent variable and the study is flexible. It concentrates on establishing the cause, relationship or associations.
The study by Everett, Xiong, Sinha, and Fricker (2013) is an ex-post factor evaluation of Indiana’s highway investment program seeking to bring out the lessons learned in the program. It seeks to show the failures and successes of the program. It offers guidelines that agencies and state departments of transport can use to move forward in validating the efficacy of investments. Also, it uses four asset categories as the sample, and they are selected based on their prevalence on the highway investment program. The study evaluates the effectiveness of each asset category, and these are pavements, bridges, mobility assets, and safety assets.
The study uses historical data to annual expenditures and annual values of performance measures to measure the variables. It collects data from official reports about projects within the Indiana department of transport. Afterward, it looks at the relationship between performance and expenditure. The analysis happens by linear regression with the physical and operational ex-post-facto assessment helping to estimate the economic impacts of total investment in all the asset categories reviewed. The study limits its focus on economic impacts.
Key findings
The key findings are that impacts are long-term and difficult to assess quantitatively. The performance and expenditure have different relationships at different interstate, NHS roadways, and non-NHS roadways. Given that road development policies, funding structures and operation of departments of transport are similar for many states, the findings can apply to many states. Besides, they can be enhanced by using comparable data from multiple states.
Descriptive
The study describes the status of an identified variable and offers systematic information about a phenomenon. A hypothesis could be developed after the collection of data. There are careful selection and measurement of each variable together with the analysis and synthesis of data lead to the testing of the hypothesis.
Beck, Legleye, Chomynova, and Miller (2014) selected a group of 15-16-year-olds whose attitudes were out of line with prevailing norms toward drug use. This was according to analyzed data from the 2003 European School Survey on Alcohol and Other Drugs (ESPAD). They pick data from the study, which was reported from Bulgaria, Czech Republic, Hungary, France, Malta, Sweden, Slovenia, and the UK. The study explored rare answers as to its primary objective.
It runs a cluster analysis to explore what leads a pupil in a typical perception of risk about the risk perceptions of alcohol, tobacco, and cannabis use. It comes up with six clusters that classify students who do not respond, deny risk, do not know the risks, sees a great risk in drug use, and see regular use as a significant risk, and see moderate risk in most incidences of use. The researchers measure the non-responders, the risk deniers and the ignorant of the risk to report that they make up on 16.9 percent of the total sample.
The risk perceptions are the dependent variables while the independent variables are gender, country, alcohol use, cannabis use, tobacco use and the clusters based on them. The findings of the study also show that country context and micro context, which infer to the frequencies of drug use, peers’ lifestyle or the extent of parental acceptance of behavior contributes to the risk perception of drug use.
References
Athur, C. A., & Hardy, L. (2014). Tranformational leadership: a quasi-experimental study. Leadership & Organization Development Journal, 35(1), 44-68.
Beck, F., Legleye, S., Chomynova, P., & Miller, P. (2014). A quantitative exploration of attitudes out of line with the prevailing norms toward alcohol, tobacco, and cannabis use among european students. Substance Use and Misuse, 49(7), 877-890.
Everett, S., Xiong, Y., Sinha, K., & Ficker, J. (2013). Ex post facto evaluation of Indiana’s highway investment program: Lessons learned. Transportation Research Record: Journal of the Transportation Research Board, 2345(2345), 24-30.
Mago, S. D. (2014). The multiplier effect: A classroom exercise. American Economist, 59(2), 182-194.