The Goal of Quantitative - Research Paper Example

? Assessing and Recommending Quantitative Research Designs submitted Quantitative research is the organized empiricalstudy of social phenomena through computational, statistical, and mathematical techniques. The goal of quantitative research is to employ and postulate hypothesis, theories, and mathematical models relating to phenomena. Measurement is the main key to quantitative research because it offers the fundamental links between empirical observation and mathematical communication of quantitative correlation. Quantitative data is any information that is in numerical system such as percentages, statistics, etc. (Wildemuth, 2009). For instance, this can be seen where by the researcher asks a specific, narrow question and gathers numerical data from the contributors to answer the question. Then the researcher analyzes the information with the aid of statistics. The researcher in this case is hoping the data to produce an unbiased outcome that can be generalized to a larger population. On the other hand, quantitative research asks comprehensive questions and gathers word information from contributors (Creswell, & Creswell, 2009). This can be seen where by the researcher search for themes and designates the data in themes and patterns elite to that of contributors. Quantitative research is mostly used in social sciences such as marketing, economics, political sciences, sociology, information technology, and psychology. In social science, quantitative research relates to empirical techniques, originating in both histories of statistics and philosophical positivism, which defers from qualitative research methods. Quantitative methods provide information only on the specific cases researched, and more general inferences are only hypothesis (Wildemuth, 2009). There are various methods/designs used in quantitative research. Some of the methods used in quantitative research are discussed in the following paragraphs; however, this paper focuses on cross sectional design (Creswell, & Creswell, 2009). The first quantitative deign is the experimental research designs. This is a social research design where a particular degree control over a provided set of variables, is applied by the researcher when conducting an exploration. This design is used to examine new hypothesis or prevailing concepts. It starts off with a concern statement, a hypothesis is postulated, and then an experiment is conducted to figure out if the hypothesis is wrong or correct (Johnson, & Reynolds, 2012). The outcomes are then analyzed using statistics that establish the basis in coming up with the inference. However, there are weaknesses to the use of experimental design when a researcher cannot regulate the situations that are substantial for the solution of the problem. In social science very little use can be applied of the design of a controlled experiment except in working with particular relationships that involve small population (Johnson, & Reynolds, 2012). In this case, experimental research design is inappropriate to use as it is expensive and difficult to conduct. The second quantitative research design is the case study research design. This research designs are objected on a single case rather than working with a sample of an enormous population. For instance, a careful identification of the factors that led to an achievement or failure of a society projected may be carried out. The preceding requisite is a limiting impact in usefulness of the case study design (Rico, & Aljian, 2009). For example, to study and define the class structure of a place of study can be expensive, difficult, and prohibitive, and consequently impractical. This design is not perfect to use as it is prohibitive and expensive. The third design used in the quantitative research is the longitudinal research design. It involves gathering of information over a period of time. This design is subdivided into cohort study, panel study, and trend study. A trend study is a longitudinal research design that aspect into the dynamics of certain features of the population over time. For instance, a researcher might decide to study an individual’s preference for projects, in their society. On the other hand, cohort study is a longitudinal research design wherein a cohort is traced over a period of time (Rico, & Aljian, 2009). A cohort is a crowd of folks who share a particular moment together during a particular period of time, for instance, a group of certain indigenous folks staying in the forest for years (Bordens, & Abbott, 2011). In addition to the longitudinal research design is the panel study. Panel study involves gathering of data from the panel over several points in time, by measuring certain dependent variable recognized by the researcher, to attain a research objective (Bordens, & Abbott, 2011). Panel study is often carried out when it is hard to analyze a case study which is a one-short project. For instance, cause-effect relationship may be researched between the number of faculty study outputs and the amount of time offered for the study as work load over four years. Longitudinal design has some limitation in terms of sustaining subjects in the research and the longer the time between points at which stages of the research are conducted, the bigger the difficulty in keeping the subjects (Bordens, & Abbott, 2011). Longitudinal research design is not suitable to use as it time consuming. Last and foremost quantitative research design is the cross-sectional research design. It is a common design postulated by researchers. It collects data from a cross-sectional of a group (Bordens, & Abbott, 2011). For instance, a contingent valuation research asks a sample of a group concerning their willingness to pay to preserve a provided forest ecosystem handy to them. Choosing the cross-sectional research design enables the researcher to obtain a better acknowledgement of social phenomena. Thus, understanding cross-sectional research design is a requirement for a well-guided research study. Cross-sectional research design is appropriate for a research study over the other research designs for several reasons. It is relatively fast and undemanding to carry out, and the data on all variables can only be gathered once (Rico, & Aljian, 2009). In cross-sectional research design, it is easy to measure the prevalence for all factors under the study. The researcher with this design can study multiple outcomes and exposures. Also, cross-sectional design is good for descriptive analyses and for postulating hypothesis (Rico, & Aljian, 2009). However cross-sectional design has several weaknesses. It is hard to make casual conclusions. In some cases, the design may give differing outcomes if another time-frame had been selected. Given prevalence, researcher has to devote considerable attention to enhancing the validity of research, including response bias, item construction, non-response bias, informant qualification, reliability assessment, and constructive validation (Johnson, & Reynolds, 2012). There are several threats to validity to be put in consideration when using cross-sectional design. In the case of cross-sectional design, two issues dominate: (1) Common method variance (CMV); this is a systematic procedure error due to application of a single source. (2) Casual inference (CI); this is the ability to deduce causation from observed empirical relations. These issues are convolutedly related; CMV bias strictly limits researcher’s ability to draw CI and postulates potential rival explanations (Bordens, & Abbott, 2011). When the two issues are combined, they present a severe hazard to the validity of the study. The researcher should widely view these issues as prone to CMV bias and incapable of underlying insights. This evolving concern about the validity of cross-sectional research design is an essential issue as this design represents the most common form of empirical studies in many fields such as marketing channels. There are three distinct data collection approaches a researcher can apply in order to lessen the threat of CMV and enhance CI, thus improving the internal and external validity. (1) Applying multiple respondents. (2) Gathering multiple types of data. (3) collecting data over multiple time periods (Johnson, & Reynolds, 2012). These approaches are capable of building separation between the obtaining of independent and dependent variables, wherein in theory, should lessen the hazards of CMV and improve CI as a result. In addition, most CMV and CI studies emphasize analytical solutions to these validity threats (Creswell, & Creswell, 2009). In conclusion, cross-sectional researches are conducted at one point. They are usually carried out to estimate the prevalence of the results of interest for a provided population, mostly for the resolution of public health organization (Creswell, & Creswell, 2009). Information can also be gathered on the person’s characteristics, including disclosure to risk factors, alongside information about outcome. In this case, cross-sectional researches provide a snapshot of the results and the characteristics linked to it, at a certain point. It is used when the goal of the research is descriptive, usually in the form of a survey (Wildemuth, 2009). Most of the time there is no hypothesis used; however, the objective is to define a population with respect to an outcome. In addition, cross-sectional research design can be used where the study is objected to find out the prevalence of the result of curiosity, for the people at a given period of time. References Bordens, K. S., & Abbott, B. B. (2011). Research design and methods: a process approach (8th ed.). New York: McGraw-Hill. Creswell, J. W., & Creswell, J. W. (2009). Research design: qualitative, quantitative, and mixed methods approaches (3rd ed.). Los Angeles: Sage. Johnson, J. B., & Reynolds, H. T. (2012). Political science research methods (7th ed.). Los Angeles: CQ Press. Rico, K., & Aljian, P. (2009). The research process research design. New York, NY: Insight Media. Wildemuth, B. M. (2009). Presentations of social study process to questions in data and library science. Westport, Conn.: Libraries Unlimited. Read More