Errors in Social Science Research - Free Research Paper Example

Published: 2022-06-03
Errors in Social Science Research - Free Research Paper Example
Type of paper:  Research paper
Categories:  Research
Pages: 7
Wordcount: 1822 words
16 min read

Social science primarily consists of psychology, sociology, economics, political science, and anthropology among various others. In particular social science research primarily investigated human behavior and also works to answer the numerous questions that researchers have with regard to human behavior. Yates (2003), contends that, through scientific study, the core intent of social science research is to understand the whys and the hows of human behavior. Nonetheless, this kind of research is associated with numerous errors that are said to be caused by various research-related factors. This being said, the primary intent of this research paper is to discuss the main sources of errors in social science research.

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A growing body of research substantiates that social science findings in researches done in fields ranging from psychology to economics are plagued by a vast range of errors and mistakes. McNabb (2013) in a report titled, "Promoting Transparency in Social Science Research" pointed out that some of the most common errors reported in social science research are made when researchers are executing and analyzing experiments. However, these errors have significant negative impacts on the conclusions of the study. Besides, the article states that social science research has three main problems associated with it. In essence, these research methods have problems such as the failure to predetermine the hypothesis that will be tested, failure to disclose information regarding every aspect of the study, and finally the failure to release all the data after publications. Thus, all of these problems are said to lead to various social science research related errors primarily.

Experimental Errors

Like in various other types of researches, no set of experimental data is perfect. Primarily, researchers are aware that this kind of data contains some margin error and many potential sources of errors in such cases are said to have significant negative impacts on the results of the study, in the sense that, they results can be incorrectly interpreted. According to Wheeldon and Ahlberg, (2012) the following are the three most basic types of errors that are mostly experienced during social science research; systematic error, human error, random error.

Systematic Errors

Usually, systematic errors in social science research result from the manner in which the experiment was conducted or even from the design of the experiment. Owing to the fact that these types of errors are inherent in the usual experimental setup, they particularly skew the research data consistently, in one direction. An excellent example of this kind of error can be seen when a researcher is trying to identify or measure the relationship between a person's behavior and the environment in which they live in. In such a case, challenges such as inconsistencies or the unreliability of the environment in which the person lives in may bias the results identified in the person's behavior. Thus, in such a case scholars contend that systematic errors that result from the external factors of an experiment can only be rectified through the change of the experimental setup.

Human Errors

Unlike the systematic errors, human errors are said to occur when the researcher or the team that takes part in the research conduct make mistakes such as setting up the experiment wrongly or misreading a certain instrument used in the study. However, Rosenberger and Stanley (2006) argue that although there are various reasons why this should not be considered as an experimental error, especially owing to the fact that it is as a result of an actual human mistake, it has negative impacts on the result findings, nonetheless.

Random Errors

In social science research, random errors, which are said to be overly unpredictable are characterized by chance variations in the measurements that the researchers have very little to no control over. Wheeldon and Ahlberg (2012) argues that, the probability of random errors occurring is equally as likely to happen as they are to be low. This, in essence, is a characteristic that primarily helps to reduce their impact especially if the data set is large since the results can be averaged. For instance, in the course of experimental research, measurements that are done using a stopwatch may have a slight variance, that may be shorter or longer than the actual or the estimated amount of time.

Sources of Error in Market Research Data

In the field of marketing, market research is one of the most significant ways that a company can best understand its customers. More fundamentally, insights will always help an organization or a marketing firm to develop messaging as well as programs to market and to sell their products successfully. However, the measurement of values in this kind of research is subject to various random errors that are said to arise from the judgments and the numerous technical assumptions that are required by those who carry out the study.

Errors of Inference

Scholars contend that in research when confidence and significance levels are set, this means allowing or instead accepting a certain level of error. Notably, when one type of error is realized in research, the null hypothesis is rejected. This, in essence, means that there is a real difference when in actuality but this kind of difference is not real but is due to the sampling and chance variation which is equivalent to stating that the treatment was effective while it was not. Law (2004) states that in most cases, errors of inference can be serious owing to the fact that researcher usually relies on findings as the primary representatives of the facts. For instance, in a case study, it is crucial that the researchers are sure a certain treatment is effective and is making the desired difference. Take, for instance, important decisions such as building special equipment into a classroom building or even adopting an expensive way of teaching reading; it is preferable to rely on the accumulated evidence rather than one particular study. However, in this regard, if one must depend on a single study, it is essential to reduce Type 1 error, and this is done efficiently through using a confidence level of 99 or even 99.9 (McNabb, 2013).

In research conducted with regard to benefit transfers, there are certain sources of errors that have significant impacts on the accuracy of the benefit transfers. These include measurement errors, generalization errors, publication selection bias. More specifically, Rosenberger and Stanley (2006) highlight that these three types of errors have different sources. To begin with. The measurement errors are deemed endogenous to the primary research, and they are only weakly controlled through the benefits transfer analyst. On the other hand, unlike the generalization errors, the generalization errors are said to arise from the process of benefit transfer application itself. Finally, the publication selection bias, unlike the two other errors, originates from the literature or from the body of knowledge, especially if the selection criteria is in favor or the results that are deemed statistically significant.

Measurement Error

In social science research, the measurement of values consists of random errors and various other research judgments that have significant effects on the results of the primary studies. Precisely, the empirical estimation of a theoretical model, in this regard, consists of decisions that relate to which data is most relevant, how it should be adjusted, which estimation strategies are least biased, and finally, the kind of assumptions that should be relied upon when connecting the data to the appropriate model. Besides, measurement errors occur when the decision of the researchers affect the accuracy of the transferability of values. Many at times, the meta-analysis finds that the methodological choices made by researchers in the estimation and the analysis of values have a statistically significant impact on the findings of the specific study. For instance, there are several methodological factors which have been found to be statistically significant in a previous meta-regression analysis of the recreation use values. This is inclusive of valuation methods, survey design, elicitation method, and most importantly, the units of measurements.

Generalization Error

In social science research, generalization errors are the primary errors that are said to arise when estimates that are done from study sites are adapted to be a representation of different policy sites. According to Rosenberger and Stanley (2006) the generalization errors in such a study usually are inversely related to the degree of correspondence between the policy and the study site. Particularly, in benefit transfer, it is assumed that there is an underlying meta-valuation faction that serves as a bridge between the values of a resource or activities, to the various characteristics of markets and sites across space over time.

Publication Selection Bias

The errors that are associated with publication selection bias mean that the empirical literature is not considered an unbiased sample of empirical evidence. Preferably, with publication selection, there is a certain kind of preference for the statistically significant results or those results that conform to theoretical expectations. For instance, while conducting a particular kind of study, the price elasticities of water demand are said to have been found to be exaggerated four-fold through publication selection bias. Based on this context, Begg and Berlin (1988) argue that a majority of the economists and meta-analysts make use of the negative sign of an own-price elasticity as a specification criterion. They also re-specify the demand relation and also re-estimate it when a positive elasticity is found.

In the same vein, a growing body of research substantiates that biases during research are also familiar sources of errors, especially from a social science perspective. In particular, Begg and Berlin (1988) describe research related biases as the intentional or the unintentional influence, which the researcher may have on a certain kind of study. This being said, it is evident that bias will prejudice the results of a certain type of research findings. For instance, when conducting a study from any social science subject, preference will be defined as the systematic error that is introduced into the sampling or testing. This, in essence, encourages the occurrence of one outcome over another. Also, owing to the fact that there is a probability of the occurrence of some degree of bias in every research project, studies substantiate that the primary concern will not be whether or not there is a bias, but rather, the extent to which bias influences the results. According to Shrestha and Loomis, (2001) there are several types of common sources of bias that may occur in a social science experimental research.

Selection Bias

Owing to the fact that social science research primarily centers on the study of human behavior, selection bias is said to occur when the participants in a specific study are not equally or randomly assigned to the experimental and control groups. Besides, in a study aimed at investigating the primary causes of research biases, Shrestha and Loomis (2001) found out that selection bias may occur in the way that test subjects are chosen to participate. Therefore, every member of a study's target population ought to have an equal chance of being selected and an equal chance of being chosen and placed into any group.

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