Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data. Descriptive Statistics are used to present quantitative descriptions in a manageable form. In a research study we may have lots of measures. Or we may measure a large number of people on any measure. Descriptive statistics help us to simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary.
For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average. This single number is simply the number of hits divided by the number of times at bat reported to three significant digits. A batter who is hitting. The single number describes a large number of discrete events. This single number describes the general performance of a student across a potentially wide range of course experiences.
Every time you try to describe a large set of observations with a single indicator you run the risk of distorting the original data or losing important detail. The batting average doesn't tell you whether the batter is hitting home runs or singles. It doesn't tell whether she's been in a slump or on a streak. The GPA doesn't tell you whether the student was in difficult courses or easy ones, or whether they were courses in their major field or in other disciplines.
Even given these limitations, descriptive statistics provide a powerful summary that may enable comparisons across people or other units. Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that we tend to look at: In most situations, we would describe all three of these characteristics for each of the variables in our study. The distribution is a summary of the frequency of individual values or ranges of values for a variable.
The simplest distribution would list every value of a variable and the number of persons who had each value. For instance, a typical way to describe the distribution of college students is by year in college, listing the number or percent of students at each of the four years. Or, we describe gender by listing the number or percent of males and females. In these cases, the variable has few enough values that we can list each one and summarize how many sample cases had the value.
But what do we do for a variable like income or GPA? With these variables there can be a large number of possible values, with relatively few people having each one.
In this case, we group the raw scores into categories according to ranges of values. For instance, we might look at GPA according to the letter grade ranges. Or, we might group income into four or five ranges of income values.
One of the most common ways to describe a single variable is with a frequency distribution. Depending on the particular variable, all of the data values may be represented, or you may group the values into categories first e.
Rather, the value are grouped into ranges and the frequencies determined. Frequency distributions can be depicted in two ways, as a table or as a graph.
Table 1 shows an age frequency distribution with five categories of age ranges defined. The same frequency distribution can be depicted in a graph as shown in Figure 1. This type of graph is often referred to as a histogram or bar chart. Frequency distribution bar chart. Distributions may also be displayed using percentages. For example, you could use percentages to describe the:.
The central tendency of a distribution is an estimate of the "center" of a distribution of values. There are three major types of estimates of central tendency:. The Mean or average is probably the most commonly used method of describing central tendency.
To compute the mean all you do is add up all the values and divide by the number of values. For example, the mean or average quiz score is determined by summing all the scores and dividing by the number of students taking the exam. For example, consider the test score values:. The Median is the score found at the exact middle of the set of values.
One way to compute the median is to list all scores in numerical order, and then locate the score in the center of the sample. For example, if there are scores in the list, score would be the median.
If we order the 8 scores shown above, we would get:. Sigmund Freud, for example, formulated psychoanalytic theory after many years of treating and studying patients with emotional problems. Although valuable information about certain types of problems may be obtained by this method, the procedure is time consuming, and it is difficult to obtain data from a broad sampling of people.
In a survey , people from a wide sample are asked questions about the topic of concern. Surveys can supply useful information, but they have their problems and limitations.
For example, the people who respond may not be representative of the population in general, or those polled may be reluctant to respond to questionnaires or to answer them accurately. In another approach to gathering information, naturalistic observation , people or animals are observed in their everyday behaviors, and their behaviors of interest are documented. For example, valuable information on wild animals, such as lions, has come from studying them in their natural habitats as opposed to observing them in a zoo because their zoo behavior may be quite different from their natural behavior.
Similarly, the behavior of a human in a home environment may differ considerably from that in a laboratory. Many standardized procedures tests have been developed to measure specific behaviors or characteristics of organisms.
Most of us have been subjected to such tests—for example, the intelligence, aptitude, and achievement tests used to predict behaviors. To be useful, tests must be both reliable and valid. Correlation , a statistical measure of a relationship between two or more variables, gives an indication of how one variable may predict another.
Descriptive research methods are pretty much as they sound -- they describe situations. They do not make accurate predictions, and they do not determine cause and effect. There are three main types of descriptive methods: observational methods, case-study methods and survey methods.
Descriptive research is a study designed to depict the participants in an accurate way. More simply put, descriptive research is all about describing people who take part in the study. More simply put, descriptive research is all about describing people who take part in the study.
All descriptive correlational method studies have the same basic property of avoiding any direct changes in the environment of the study. However, there are a number of different types of descriptive correlational methods that each perform research in a slightly different way. Definition of Descriptive Research. By the term descriptive research, we mean a type of conclusive research study which is concerned with describing the characteristics of a particular individual or group. It includes research related to specific predictions, features .
Psychology Definition of DESCRIPTIVE RESEARCH: An empirical investigation to test a hypothesis or to look at conditions, relationships. Descriptive research can be explained as a statement of affairs as they are at present with the researcher having no control over variable. Moreover, “descriptive studies may be characterised as simply the attempt to determine, describe or identify what is, while analytical research attempts to.