Data interpretation Research bias

It is necessary for researchers to ensure that they avoid research bias. Biases are likely to occur when a researcher is evaluating the results of the data collected. Bias normally results from the systematic alteration of the truth. However, a researcher may avoid biases when evaluating the results of data collected through ensuring that they use the collect statistical techniques. The researcher may be able to avoid biases through ensuring that they clearly understand all the statistical techniques that they are planning to use when collecting data. According to Gerhard (2008), researchers can avoid bias through multivariate analysis. The multivariate analysis normally applies mathematical models in assessing the association of multiple predictors of an outcome. In this case, the estimate for every variable reflects the individual association of the variable with the outcome. The use of the regression model during analysis tends to produce unbiased results for the variable of interest when all the confounders adequately measured and the model correctly specified. In this way, the research will be able to avoid biases when they are evaluating the results. Triangulation is also another strategy for avoiding bias in research (Liu & Fellows 2009). The research may verify the results using other data sources. A research can find other data sources that support the results of the data collected. In this way, it is possible to ensure confidence and avoid bias.

Sample size
During a research, using the correct sample size is normally crucial. A sample that is too big can lead to a waste of precious research resources such as money and time, and a sample that is too small may not allow the research to gain reliable insights. Scholars should determine the sample size of for their research by determining how they want their study results to match that of the entire population (Parket & Rea 2014). The sample size must be representative in that it will allow the research to generalize the study findings to the wider population. So as to determine how large or small the sample size of the study, the research should do the margin of error and confidence level. In the margin of error, there is no sample that is perfect; however, one must decide how much error to allow (LeBlanc, 2004). The margin of error determines how much lower or higher than the population mean a research is willing to let the sample mean fall. The researcher also uses the confidence level in determining how often the population percentage lies within the boundaries of the margin of error.

Limitations normally affect almost all research projects, and they are the potential weaknesses that are mostly out of the researcher’s control (Gerhard, 2008). It is necessary to know that limitations of a dissertation are normally not something that the researcher can resolve. Thus, even with the obvious limitations, it is acceptable to consider the study and the results of the study authentic. When conducting a study, it is necessary to remember that whatever limits you also limit other researchers. A research study might have limitations such as having access to just one certain people in the organization, certain data, and certain documents. These are some obvious limitations in a research study; however, that does not mean that the study results are not authentic. There will always be some limitations when one is conducting the study; however, those limitations do not make it unacceptable to use the study results (Wiersma, 2000). In this case, the researcher can use the study results and apply to that particular group of people despite that the study focused.

Evaluating data
Data evaluation usually helps the researcher to gain an understanding of the collected data. When evaluating data that is contrary to the results of other studies on the topic, the researcher must consider several factors. In this case, it is significant to consider, what the researcher was trying to learn, whether the study collected the relevant information if data is sensible, and if the right things were measured. During evaluation of the data, the researcher must ensure that he considers the variables included in the study when evaluating the data (Grinnell & Unrau 2010). The data that the researcher garnered from the research participants is what will help the researcher to understand the data collected. When evaluating data that is contrary to the results of other findings on the topic, the researcher can also consider determining some of the factors that would have caused the different. It might be the environment, the time span, or even the people involved in the study (Gerhard, 2008). Hence, putting these factors into consideration when evaluating data will help one to understand the data and also know why the findings may differ despite the topic being the same. The time span can be a major reason for the difference; thus, when evaluating data, the researcher should consider the time difference in the study. Maybe the studies done on the topic were several years ago and because of the current changes it may be the cause of the difference.