Experimental Quantitative Correlation Study Number

ExperimentalQuantitative Correlation Study

Number:

ExperimentalQuantitative Correlation Study

Aresearch plan can be defined as a detailed outline that analyses howas well as the steps that should be taken when carrying out aninvestigation. It entails how data will be collected, instrument tobe used how the instruments will be used, and finally, the means toanalyze the data collected. There are different types of researchdesigns that can be implemented at any one given time however, itdepends with the type of research material available, intendedresults, as well as time need to carry out the research (Zachariadiset al, 2013).

Thekey functions of a research blueprint are to ensure that the evidencecollected enables the researcher to effectively address the researchproblem. It encompasses the sketch out for the compilation,measurement, and data analyzing process. However, research problemdetermine the type of design you can use, not the other way around.This paper looks at the quantitativecorrelation studyas a research design, its components, whether it is experimental orobservational, why it would be good to use this design as well as itslimitations (Duvendack &amp Palmer-Jones, 2013).

Introduction

Experimentalquantitative correlation study,this is the systematic scientific investigation of data as well astheir relationship. There are different types of quantitative studydesign, and the one in question here is the experimental researchquantitative correlation study. These rae also known as ‘trueexperimentation’ and uses scientific approach in identifying thecause effect relationship in a given set of variables. The mainexperiment is carried on variables, where an independent variable iscontrolled to determine the effects on the dependent variables.Example of such questions include where qualitative research can beapplied include ‘the effect of positive strengthening on attitudetoward school’ the approach is designed to find out the effects ofalleged causes(Duvendack&amp Palmer-Jones, 2013).

Componentsof Experimental quantitative correlation study

Theexperiments quantitative correlation research design comprises ofdifferent components. One of the key components in this design is theexperimenting variables.These are comprised of the dependent and independent variables (thecause is controlled by the researcher). There is also the controland experimental group.There is also the set hypothesiswhich acts as the key question on research. The experiment is carriedto proof the hypothesis whether true or false. Finally, it’sreferred as experimental as the researcher can make conclusions aboutcausality. As well as be used to examine relations among manyvariables (Level&amp Waters, 1999).

Reasonbehind whether the approach is experimental or observational

Thisdesign would be considered both observational and experimental it ismore experimental as it involves controlling the variables. It isobservational because the researcher at some point observes thevariables without intervening, however, the despite beingobservational, the design is more experimental. It is said t beexperimental as measures are taken before and after a treatment isgiven, for example is when there is a time series experiment. It alsoinvolves measuring whether it’s making a positive or a negativecorrelation. Correlation research on the other hand, involves thequantitatively studying the associations/relationsamong and between variables (Zachariadis et al, 2013).

Reasonbehind the Experimental Quantitative Approach Choice

Thekey reason behind the choice behind this design is due to itsnumerous merits as well as the ability to have a direct control ofthe experiment (variables), and benefit of this research design(experimental approach) is the capability to maneuver specificallyone or more variables of the research. Compared to other researchdesigns, the experimental researchers have strong advantages. Onesuch advantage is the fact that, the cause-and-effect approachenables a high level of control as well as the capacity to reproducethe study in almost exact circumstances. This is a commanding form ofverification in the aspect of science and in turn should be marked asdefinitive merit to experimental research design. There is theability to manipulate variables. The design also involves hardnumbers that are provable results. Moreover, the approach enables theresearcher to analyze and at the same time explore the variable andthe results, it gives a broader perspective, eliminates personalbiasness when making conclusions (Venkatesh et al, 2013).

Biasin experimental quantitative

Thereare different types of biasness relate to the experimentalquantitative research design. One such biasness is the selectionbias, channeling bias, and randomization. Another bias is experiencedas sampling bias, omission and inclusive bias, as well as proceduralbias. Biasness is usually experience when the researcher wants toportray a certain outcome from the research. When using this design,biasness can be brought about by experimental error and also failureto take into account all the possible variables. Biasness is onedisadvantage to the quantitative research approach over othermethods. The most vulnerable, biasness is the measurement bias. Thiscomes along when the researcher failsto manage the effects of data gathering/collection and measurement(Level&amp Waters, 1999).

Inconclusion, the experimental quantitative research approach isdesigned to determine the effect of apparent causes. It is clear thatone variable is varied and its effect recorded on another. The designcan scientific and non-scientific, whereby the scientific tries togive results free of bias. The approach is advantageous as differentconclusions to the hypothesis can be made. It is also clear that,Quantitative researchers are likely to reasons deductively. Finally,the researchtools are supposed to be reliable and valid. As one of the researchdesigns, the experimentalquantitative research approach can be used in different areas andgive good results when effectively used.

References

Duvendack,M., &amp Palmer-Jones, R. (2013). Replication Of Quantitative WorkIn Development

Studies:Experiences And Suggestions.Progress In Development Studies,&nbsp13(4),307-322. Doi:10.1177/1464993413490480

Level,D. A., &amp Waters, G. R. (1999). Experimental Design InCommunication Research.&nbspJournal

OfBusiness Communication,&nbsp14(1),7-12.

Venkatesh,V., Brown, S. A., &amp Bala, H. (2013). Bridging TheQualitative-Quantitative Divide:

GuidelinesFor Conducting Mixed Methods Research In Information Systems.&nbspMisQuarterly,&nbsp37(1),21-54.

Zachariadis,M., Scott, S., &amp Barrett, M. (2013). Methodological ImplicationsOf Critical

RealismFor Mixed-Methods Research.&nbspMisQuarterly,&nbsp37(3),855-879.