Quantitative research Quantitative research In my opinion, quantitative research refers to a method of conducting research using techniques that may be interpreted using numerals and may be manipulated to suit the study being carried out by the researcher, either at present or at later studies to be conducted. In this case, the researcher is able to familiarize themselves with the problem or issue at hand, figure out how to assess the same problem using experimental methods and analyze the data provided. What the researcher has observed can then be measured using scientific techniques that bring out a clear indication of the results of the expected data. Validity In the thought of Golafshani (2003), validity is a concept used in research to determine whether some research has succeeded in measuring the variables intended to be measured by the set analysis. In short, validity aims at assessing if the research has been successful or not, by analyzing the results. If the researcher applied the correct measurements while carrying their research, it is evident that the intended results will be achieved; thus, the research may be termed as valid (Golafshani, 2003). Internal validity refers to the research that allows the researcher have answers or evidence to the propositions indicted at the start of the research (Golafshani, 2003). For instance, if the researcher can prove that the outcomes are resultant form the stated inferences, then the research can be assessed as the one having strong internal validity. The researcher is advised to assess the propositions if they have to attain internal validity in their research, which are the resultants of the outcomes. In an attempt to assess the outcome of the research, the researcher has to analyze the propositions that lead to the occurrence of the outcome. Golafshani (2003) states that validity in quantitative research such as construct validity determines what kind of hypothesis, notion, question and concepts need to be assessed and how data needs to be collected so as to achieve tangible results of the research. On the other hand, statistical analysis is the one that is analyzed in mathematical processes and is presented in numbers, majorly referred to as objective hard data (Golafshani, 2003). The data arrived at is then analyzed and presented statistically, what Golafshani (2003) terms as rewards of numerals and not words. A study can be described as the component that provides the basis for an understanding of a problem or an enigma that is seemingly problematic and needs a generated study for an understanding of the underlying concepts of the problem in question. In relation to Trochim (2006), external validity refers to the definite truth of conclusions that necessitate generalizations. This is to suggest that the research conducted will be valid to persons in other areas at different times. In this case, validity can be understood to have the responsibility of making sure that the receivers of the study are provided with various ways of coming up with reality based on the suppositions made. Golafshani (2003) indicates that validity is the foundation in which an understanding of the general laws, reality, objectivity reason and mathematical data is explicated. Experimental designs As argued by Trochim (2006), research designs are helpful in determining the research project as one. Through the experimental designs, it is possible to appreciate the rationale behind the functionality, measures, and sample studies amongst others in an endeavor to address the research questions. Experimental designs apply random assignments whilst quasi-experimental designs do not apply random assignments (Trochim, 2006). A comprehension of the experimental designs is useful for an understanding of the best design to use whilst conducting research. Factorial design, as seen from the argument presented by Golafshani (2003), is the one that uses the factor as the main independent variable and the effect – as the results of the different levels of the factors. Factorial designs combine factors into one subject: for instance, summarizing the causes of dropouts in levels so as to understand the age of the dropouts. On the other hand, Yang (2008) argues that regression discontinuity design (RD) involves a probability of receiving a treatment in a discontinuous role of more than one variable. For instance, a study on the attaining of a scholarship by a student depends on the performance of the same student. In the two-group randomized design, the researcher draws two lines, one – for the randomly assigned whilst the other line represents the comparison group that is not assigned any program (Golafshani, 2003). The relative contrast between the two groups brings out an explanation of the study in question. References Golafshani, N. (2003). Understanding Reliability and Validity in Qualitative Research. The Qualitative Report, 8 (4), 597-607. Trochim, W. (2006). Research Methods. Knowledge Base. London: Web Center for Social Research Methods. Yang, M. (2008). Regression Discontinuity Design and Program Evaluation. London: ProQuest.