How to Write a Research Methodology for Your Academic Article June 21, 8 Min Read For academic writing help, focus on these criteria and tips on how to write a great research methodology for your academic article This article is part of an ongoing series on academic writing help of scholarly articles. Previous parts explored how to write an introduction for a research paper and a literature review outline and format. The Methodology section portrays the reasoning for the application of certain techniques and methods in the context of the study. The description of the methods used should include enough details so that the study can be replicated by other Researchers, or at least repeated in a similar situation or framework.
Data Preparation and Analysis Preparing Data After data collection, the researcher must prepare the data to be analyzed. Organizing the data correctly can save a lot of time and prevent mistakes.
Most researchers choose to use a database or statistical analysis program e. Once the data has been entered, it is crucial that the researcher check the data for accuracy. This can be accomplished by spot-checking a random assortment of participant data groups, but this method is not as effective as re-entering the data a second time and searching for discrepancies.
This method is particularly easy to do when using numerical data because the researcher can simply use the database program to sum the columns of the spreadsheet and then look for differences in the totals. One of the best methods of checking for accuracy is to use a specialized computer program that cross-checks double-entered data for discrepancies.
Each descriptive statistic summarizes multiple discrete data points using a single number. They can tell the researcher the central tendency of the variable, meaning the average score of a participant on a given study measure. The researcher can also determine the distribution of scores on a given study measure, or the range in which scores appear.
Additionally, descriptive statistics can be used to tell the researcher the frequency with which certain responses or scores arise on a given study measure.
This amount of information is not enough information to conclude that vision correction affects economic productivity. Inferential statistics are necessary to draw conclusions of this kind.
This means that for the most part, if a person is tall, they are likely to have a large shoe size, and conversely, if they are short, they are likely to have a smaller shoe size.
Correlation can also be negative. For example, warmer temperatures outside may be negatively correlated with the number of hot chocolates sold at a local coffee shop. This is to say that as the temperature goes up, hot chocolate sales tend to go down.
Although causality may seem to be implied in this situation, it is important to note that on a statistical level, correlation does not imply causation. A good researcher knows that there is no way to assess from correlation alone that a causal relationship exists between two variables.
Determining causation is a difficult thing to do, and it is a common mistake to assert a cause-and-effect relationship when the study methodology does not support this assertion.
Inferential Statistics Inferential statistics allow the researcher to begin making inferences about the hypothesis based on the data collected. This means that, while applying inferential statistics to data, the researcher is coming to conclusions about the population at large.
Inferential statistics seek to generalize beyond the data in the study to find patterns that ostensibly exist in the target population. This course will not address the specific types of inferential statistics available to the researcher, but a succinct and very useful summary of them, complete with step-by-step examples and helpful descriptions, is available here.
This difference must be due to the manipulation of the independent variable. No matter how well a researcher designs the study, there always exists a degree of error in the results.
Statistical significance is aimed at determining the probability that the observed result of a study was due to the influence of something other than chance.In most research studies, the analysis section follows these three phases of analysis. Descriptions of how the data were prepared tend to be brief and to focus on only the more unique aspects to your study, such as specific data transformations that are performed.
Research is the foundation of effective decision making and knowledge creation. The research process has been refined over the years to a level of sophistication that, while yielding actionable results, may appear daunting to those not immersed in its practice.
This book fills an important void in the social network literature by bringing together theory, methodology and history. Its practical and readable style gives clear guidance on how to do social network research and will be invaluable to anyone undertaking a network study.
terminology of data analysis, and be prepared to learn about using JMP for data analysis. Introduction: A Common Language for Researchers Research in the social sciences is a diverse topic. Lesson A Assessing the Methodology of the Study: There are four main aspects of the research methodology: design, sampling, data collection, the data analysis.
If inappropriate methodology is used, or if appropriate methodology is used poorly, the results of a study could be misleading. 15 Methods of Data Analysis in Qualitative Research Compiled by Donald Ratcliff 1. Typology - a classification system, taken from patterns, themes, or other kinds of.