The Importance Of Techniques In Research

Interrogating construct validity, how well the variables in the study were manipulated and measured, is also essential when dealing with causal claims (Morling, 2012). Again, because so little information from the methods section was addressed in Bergland's (2014) article, readers cannot validate the construct validly of the study. It would have been

With such a thorough conclusion comes a limitation or two which are that the group compared to the experimental group didn’t receive an intervention; also the outcomes had been based on the same sessions of observations.

The newspaper article, Vitamins Cause Crime, claims they found a positive correlation with the consumption of vitamins and crime rates. With the inappropriate claim that vitamins cause cancer, the newspaper’s article’s name is false because it falls under the Correlation Vs. Causation Fallacy and is an illusory correlation. Regardless of the newspaper’s article, a more appropriate study could be done by creating an experimentally designed double blind research project. This research project would utilize the Scientific Method, use of a control group and an experimental group, correlation analysis, and a single manipulatable independent variable. Thus creating a more credible claim regarding any correlation between vitamins and crimes rates through a double blind research study.

The validity of a study is very important, be it internal or external. Internal validity is important because without it, there is no cause and effect. Proving a relationship between two variables is good, but as we know, correlation does not necessarily imply causation. However, we often times conduct studies in order to determine cause-and-effect relationships, although at the end one is faced with the threat of accuracy. In other words, can the result of the research be generalized? Can we conclude that changes in the independent variable caused the observed changes in the dependent variable? Is the evidence for such a conclusion good or poor?

Since this study is a randomized-controlled trial, randomization eliminates confounding error by ensuring that known and unknown confounders are balanced across both treatment groups [8]. Hence, this paper discusses potential confounders to consider before randomization occurs.

Measurement performance in clinical epidemiology is described based on accuracy, reliability, range, responsiveness, and interpretability. The effectiveness of data from a study is based on performance of measurement. Thus, the data from clinical epidemiology must be accurate and reliable to achieve its goal. Validity implies that data measured has to correspond to the true value being measured. On the other hand, reliability ensures that the data recorded can be reproduced (Fletcher et al., 2012). Effective performance of measurements should not contain data

The sample appeared to be large enough to detect a hypothesized effect, however most of their hypotheses were not supported and a power analysis was not conducted or available for review.

The authors provided the information needed to address confidence in the study results. They described the risk of bias and the strength of evidence for each study. Therefore, the difference in the risk of bias explains the difference and variability in results (13). They reported moderate to high risk of bias and low level of evidence might result in decreasing the confidence in the treatment effect (15-17).

Since this is a correlational study the results do not prove causation. One issue with performing a correlation study is the fact that even though you can get valuable information from doing one it does not prove cause and effect like an experimental study would. I am not that confident in the results of this study. One reason being that they did not use a large number of people to conduct the study. Using only

Causation can be hard to prove, an example would be hypertension. Over the years there have been many things associated with the cause of hypertension such as the use of table salt, smoking, sleep apnea, alcohol, etc. To use the term causation in this example it would mean that every single person would get hypertension

Although prospective cohort studies have fewer potential sources of bias and confounding than retrospective studies, the study is still vulnerable to bias. The study is vulnerable to selection bias. Selection bias occurs in a cohort study when the loss to follow-up is related to both the exposure and outcome of interest. Loss to follow-up can arise in the study, if subjects can no longer be located or when they no longer want to participate in a study. Loss to follow-up is problematic since it can reduce the power of the study to detect associations that are truly present, and it can bias the study results. Individuals in the study can be lost due to many reasons such as personal choice to opt out of the study, death, or change in geographic location. Loss to follow-up did occurred in this study. Of the 37,500 individuals, 2,700 participants were lost during follow-up. Due to loss to follow, it can bias the results by causing the true measure of association to be either overestimated or underestimated. To reduce loss to follow, is to obtain high participation rate. High participation rates is not always feasible but most epidemiologists are satisfy with participation rates greater than 80%. The participation rate of the study is 92.8%. Although the rate is higher than 80%, it is still important to consider how this bias impacts the outcome. In addition, the study is vulnerable to information bias. Information bias is an error that is due to systematic differences in

The Comorbidity Questionnaire was the tool used to gather the original data. According to Bender et al. (2008) this tool was modeled after the Charlson Comorbidity Index (CCI). The detail of this modified tool was not disclosed nor was this author able to retrieve a copy of the tool used. However, it should be noted that the tool was approved for studies from National Institutes of Health. The CCI on the other hand is a reliable and validated tool (Hall, Ramachandran, Narayan, Jani, & Vijayakumar, 2004). Therefore, it is impossible for this author to evaluate the tool that was used to collect the initial data but instead, inferences can be superimposed that would lead the reader to assume that if the CCI is a valid and reliable tool, then the modified tool should also be valid and reliable. However, it is never proper protocol to make that kind of assumption.

Chapter 8, Post Hoc Rides Again, explains the cause and effect problem. The author uses an example about a study in which concluded that college students who smoked got lower grades than those who didn’t smoke. Everybody just assumed that cigarettes caused the bad grades, but how could anyone be sure it wasn’t the other way around? In other words, there could be the possibility that bad grades were the cause for the student to smoke. However, it is usually viewed as the way that produces less shame, or in some cases, to boost propaganda. The main point to this chapter was to show that a conclusion of a study could happen just as likely from both points of views, with the same amount of evidence to prove it. The only reason people view it the

The survey data are most from Chinese students. So this might makes the data a little bit narrow. Preparing for the survey questions are little bit long because we were not sure the questions are good for our data analysis.

With the various types of research methodologies, one can classify them as medical, social, computer, or life science research. However, in computers and bioinformatics research methodology, the methods used are computational, computer simulation, iCloud, omics, and theoretical. Before starting any research, a literature review is required, for example, magazines, books, scholarly/peer reviewed journals, company sales, customer, and employee records. If in the medical field one may need to review, medical, hospital, and clinical records. Depending on what type of research one is planning to implement, and how they plan on conducting the project, will determine the type of research methodology to utilize. In order to provide the best chance for accomplishing a research project, one should have knowledge of the several kinds of the methodologies that are attainable. Really, it does not matter what type of research is conducted, however, it is critical that the research methodology used identifies the issues and determines a precise and efficient answer to the questions being asked, or a solution to the problems at hand, from choosing a suitable methodology. In this paper we will be identify the different types of research methodologies, and identify the methods that are relevant to our current research on computer and life science, then discuss the methodology we have chosen and our reason for our choice, along with some examples in Computer Science