Even when the same information is being conveyed, different methods of presentation must be employed depending on what specific information is going to be emphasized. Inappropriately presented data fail to clearly convey information to readers and reviewers. Methods of presentation must be determined according to the data format, the method of analysis to be used, and the information to be emphasized.
Furthermore, we discuss the issues that must be addressed when presenting various kinds of information, and effective methods of presenting data, which are the end products of research, and of emphasizing specific information.ĭata can be presented in one of the three ways: We describe the roles and appropriate use of text, tables, and graphs (graphs, plots, or charts), all of which are commonly used in reports, articles, posters, and presentations. The present study does not discuss this data preparation process, which involves creating a data frame, creating/changing rows and columns, changing the level of a factor, categorical variable, coding, dummy variables, variable transformation, data transformation, missing value, outlier treatment, and noise removal. Data must be prepared in such a way they are properly recognized by the program being used. These days, data are often summarized, organized, and analyzed with statistical packages or graphics software. Once a detailed question is ready, the raw data must be prepared before processing. In other words, a well-defined question is crucial for the data to be well-understood later. A broad question results in vague answers and results that are hard to interpret. The more detailed the question is, the more detailed and clearer the results are. Planning how the data will be presented is essential before appropriately processing raw data.įirst, a question for which an answer is desired must be clearly defined. Furthermore, each data set needs to be presented in a certain way depending on what it is used for. Since most data are available to researchers in a raw format, they must be summarized, organized, and analyzed to usefully derive information from them. Whether data are being collected with a certain purpose or collected data are being utilized, questions regarding what information the data are conveying, how the data can be used, and what must be done to include more useful information must constantly be kept in mind. Moreover, as journal editors and reviewers glance at these presentations before reading the whole article, their importance cannot be ignored.ĭata are a set of facts, and provide a partial picture of reality. They can make an article easy to understand, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Text, tables, and graphs for data and information presentation are very powerful communication tools. A graph is a very effective visual tool as it displays data at a glance, facilitates comparison, and can reveal trends and relationships within the data such as changes over time, frequency distribution, and correlation or relative share of a whole. A table is best suited for representing individual information and represents both quantitative and qualitative information. Text is the principal method for explaining findings, outlining trends, and providing contextual information. In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. However, no matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers. Therefore, raw data need to be summarized, processed, and analyzed. Data are usually collected in a raw format and thus the inherent information is difficult to understand. This is again an AR(1) process, but with a faster decay, \(\rho=0.5:\) set.seed(2017)ĪR = arima.sim(model=list(ar =. Here is the simulation in R with \(\rho = 0.9:\) set.seed(2017)ĪR = arima.sim(model=list(ar =. The negative values in the plot respond to a process of the form \(\large\color+\epsilon_t.\) On this plot the ACF is significant only once (in reality the first entry in the ACF is always significant, since there is no lag in the first entry - it’s the correlation with itself), while the PACF is geometric. Also, here is a more extensive document with simulations found online.
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