The present paper aims to provide basic guidelines to present epidemiological data using tables and graphs in Dermatology. Although simple, the preparation of tables and graphs should follow basic recommendations, which make it much easier to understand the data under analysis and to promote accurate communication in science. Additionally, this paper deals with other basic concepts in epidemiology, such as variable, observation, and data, which are useful both in the exchange of information between researchers and in the planning and conception of a research project.
Among the essential stages of epidemiological research, one of the most important is the identification of data with which the researcher is working, as well as a clear and synthetic description of these data using graphs and tables.
For example, the use of a certain type of data impacts the amount of time it will take to collect the desired information throughout the field work and the selection of the most appropriate statistical tests for data analysis.
The correct preparation of tables allows researchers to present information about tens or hundreds of individuals efficiently and with significant visual appeal, making the results more easily understandable and thus more attractive to the users of the produced information.
Therefore, it is very important for the authors of scientific articles to master the preparation of tables and graphs, which Dot plot definition statistics of sexual immorality previous knowledge of data characteristics and the ability of identifying which type of table or graph is the most appropriate for the situation of interest. Before evaluating the different types of data that permeate an epidemiological study, it is worth discussing about some key concepts herein named data, variables and observations:.
Data - during field work, researchers collect information by means of questions, systematic observations, and imaging or laboratory tests. All this gathered information represents the data of the research. For example, it is possible to determine the color of an individual's skin according to Fitzpatrick classification or quantify the number of times a person uses sunscreen during summer.
If the quality of data is good, i. Observations - are measurements carried in one or more individuals, based on one or more variables. For instance, if one is working with the variable "sex" in a sample of 20 individuals and knows the exact amount of men and women in this sample 10 for each groupit can be said that this variable has 20 observations. Variables - are constituted by data.
For instance, an individual may be male or female. In this case, there are 10 observations for each sex, but "sex" is the variable that is referred to as a whole. Another example of variable is "age" in complete years, in which observations are the values 1 year, 2 years, 3 years, and so forth.
Variables are specifically divided into two large groups: However, it is important to emphasize that variables measured on a numerical scale whether discrete or continuous are richer in information and should be preferred for statistical analyses. Figure 1 shows a diagram that makes it easier to understand, identify and classify the abovementioned variables.
Firstly, it is worth emphasizing that every table or graph should be self-explanatory, i. In order to analyze the distribution of a variable, data should be organized according to the occurrence of different results in each category. As for categorical variables, frequency distributions may be presented in a table or a graph, including bar charts and pie or sector charts.
The term frequency distribution has a specific meaning, referring to the the way observations of a given variable behave in terms of its absolute, relative or cumulative frequencies. In order to synthesize information contained in a categorical variable using a table, it is important to count the number of observations in each category of the variable, thus obtaining its absolute frequencies. However, in addition to absolute frequencies, it is worth presenting its percentage values, also known as relative frequencies.
For example, table 1 expresses, in absolute and relative terms, the frequency of acne scars in year-old youngsters from a population-based study conducted in the city of Pelotas, Southern Brazil, Dot plot definition statistics of sexual immorality The same information from table 1 may be presented as a bar or a pie chart, which can be "Dot plot definition statistics of sexual immorality" considering the absolute or relative frequency of the categories.
It can be observed that, regardless of the form of presentation, the total number of observations must be mentioned, whether in the title or as part of the table or figure. Frequency distributions of numerical variables can be displayed in a table, a histogram chart, or a frequency polygon chart.
With regard to discrete variables, it is possible to present the number of observations according to the different values found in the study, as illustrated in table 2. This type of table may provide a wide range of information on the collected data. Table 2 shows the distribution of educational levels among year-old youngsters from Pelotas, Southern Dot plot definition statistics of sexual immorality, with absolute, relative, and cumulative relative frequencies.
In this case, absolute and relative frequencies correspond to the absolute number and the percentage of individuals according to their distribution for this variable, respectively, based on complete years of education.
It should be noticed that there are adolescents with 8 years of education, which corresponds to Tables may also present the cumulative relative frequency of the variable.
In this case, it was found that It is important to point that, although the same data were used, each form of presentation absolute, relative or cumulative frequency provides different information and may be used to understand frequency distribution from different perspectives. When one wants to evaluate the frequency distribution of continuous variables using tables or graphs, it is necessary to transform the variable into categories, preferably creating categories with the same size or the same amplitude.
However, in addition to this general recommendation, other basic guidelines should be followed, such as: For example, in order to categorize height in meters of a set of individuals, the first step is to identify the tallest and the shortest individual of the sample. Let us assume that the tallest individual is 1. The next step is to divide this difference by the number of categories to be "Dot plot definition statistics of sexual immorality," e.
Table 3 illustrates weight values at 18 years of age in kg continuous numerical variable obtained in a study with youngsters from Pelotas, Southern Brazil. Therefore, it is possible to observe that data from continuous numerical variables may be presented in tables or graphs.
Weight distribution at 18 years of age among youngsters from the city of Pelotas. The forms of data presentation that have been described up to this point illustrated the distribution of a given variable, whether categorical or numerical. In addition, it is possible to present the relationship between two variables of interest, either categorical or numerical.
The relationship between categorical variables may be investigated using a contingency table, which has the purpose of analyzing the association between two or more variables.
The lines of this type of table usually display the exposure variable independent variableand the columns, the outcome variable Dot plot definition statistics of sexual immorality variable.
For example, in order to study the effect of sun exposure exposure variable on the development of skin cancer outcome variableit is possible to place the variable sun exposure on the lines and the variable skin cancer on the columns of a contingency table.
Tables may be easier to understand by including total values in lines and columns. It is such a display of percentage values that will make it possible for risk or exposure groups to be compared with each other, in order to investigate whether individuals exposed to a given risk factor show higher frequency of the disease of interest.
Thus, table 4 shows that Another of interpreting this table is observing that This form of presentation is one of the most used in the literature and makes the table easier to read.