Descriptive statistics in psychology

Statistics is the branch of mathematics that studies variability, as well as the process that generates it following probability laws. It is necessary both to carry out research and to understand how research is currently being done. beyond the conclusions of any study. Thus, knowledge in this branch will allow us to know to a large extent the quality of a study and therefore the degree of reliability that its conclusions deserve.

Descriptive statistics, for its part, is that part of statistics that It is responsible for collecting, presenting and characterizing a set of data. In other words, descriptive statistics tries to know what has happened, compared to inferential statistics that tries to predict what will happen in the future under a set of conditions.

For example, these conditions are usually specified by variables such as age, climate or degree of anxiety. Thus, descriptive statistics in psychology has the objective of summarizing in a way useful to the researcher and the reader what has happened in a given study.

As we have said before, variables are one of the central axes of descriptive statistics – and non-descriptive statistics as well. A variable encompasses a set of values, and depending on these values ​​we can talk about:

Variables quantitative: they can have numerical value (age, price of a product, annual income). Categorical variables or qualitative: cannot be measured numerically (such as sex, nationality or skin color) or scale directly.

The variables can also be classified into:

One-dimensional variables. they only collect information about a characteristic of a population. For example, height of students in a school.Two-dimensional variables. they collect information about two characteristics of the population. For example, height and age of students at a school.Multidimensional variables. collect information about three or more characteristics of a population. For example, height, weight and age of students at a school.

Thus, the data (numbers or measurements collected from observation) can be of two types:

Data discreet. They are numerical responses that arise from a counting process.Data continuous. They are numerical responses that arise from a measurement process.

Measurement scales in descriptive statistics

Measure is the process of linking abstract concepts with empirical indicators . The result of measurement is called extent.

There are four possible measurement scales, which are used to assist in the variable classification. In this sense, the properties of reliability and validity They are very important in descriptive statistics, since they tell us about the quality of the measurement. Because, what good are data that are incorrectly taken from the source?

Nominal scale

On this scale Numbers are assigned to categories that do not need an order (we cannot say that one category is more than another). Furthermore, these categories are mutually exclusive. An example of this could be the gender or color. Thus, the chosen option would be exclusive of the others.

This scale is assigned to the variables qualitative or categorical.

ordinal scale

Here categories are established with two or more levels that imply an order among themselves. As in the previous scale, these are also mutually exclusive categories, but now we can place the values ​​of the variables in an order. For example, this scale could be seen in responses to a questionnaire:

Totally disagree.In disagreement.Indifferent.OK.Totally agree.

These response options can be coded with numbers ranging from one to five that suggest a preset order. However, we cannot know, unless we use advanced statistical procedures and try to estimate it, the distance between two categories. Thus, we can talk about the object of research having more or less of something, but in a simple way we cannot talk about how much more of that something (intelligence, memory, anxiety, etc.).

This scale is also assigned to the variables qualitative.

Interval scale

This scale quantifies the distance between the values. The interval measurement also has the characteristics of the two previous measurements. Thus, it establishes the distance between one measurement and another.

The interval scale is applied to continuous variables. However, It is not possible on this scale. Absolute zero. A clear example of this type of measurement is a thermometer. When it reads zero degrees, it does not mean an absence of temperature.

This scale is applied in variables quantitative.

Ratio scale

Finally, this scale includes the characteristics of the previous ones. Determine the exact distance between intervals of a category. Furthermore, it has a point of absolute zero in which the characteristic or attribute being measured does not exist. For example, the number of children: zero children means no children.

This scale is applied in variables quantitative.

Frequencies in descriptive statistics

A frequency distribution is a list of the possible values ​​(or intervals) that a variable takesalong with the number of observations for each value.

The Absolute frecuency register the number of times a given value appears among observations.The relative frequency register the proportion or percentage of occurrence of a certain value of observations.

This frequency distribution is usually represented by boards. Thus, it must include all possible values ​​of a variable. In addition, the total number of observations must be indicated (n) that have been made. When we have a large amount of data categories and some of them with very low frequencies should be grouped into intervals.

Indicators

Finally, indicators in statistics are used to describe a set of data using a number. Thus, this number summarizes a characteristic of the distribution of the data analyzed. Some of these indicators are:

Indicators of the central tendencyMean or average. Mode. Median. Indicators of dispersionVariance.Minimum/Maximum.Range.Interquartile range.

Thus, with the help of these concepts, descriptive statistics is responsible for purifying, organizing and calculating statistics and representations of data to offer the researcher, and by extension the scientific community, a complete map of what has happened in your studio.

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