Many scientific disciplines, especially social science and psychology, use this method to obtain a general overview of the subject. Some subjects cannot be observed in any other way; for example, a social case study of an individual subject is a descriptive research design and allows observation without affecting normal behavior.
It is also useful where it is not possible to test and measure the large number of samples needed for more quantitative types of experimentation. These types of experiments are often used by anthropologists, psychologists and social scientists to observe natural behaviors without affecting them in any way.
It is also used by market researchers to judge the habits of customers, or by companies wishing to judge the morale of staff. The results from a descriptive research can in no way be used as a definitive answer or to disprove a hypothesis but, if the limitations are understood, they can still be a useful tool in many areas of scientific research. The subject is being observed in a completely natural and unchanged natural environment.
A good example of this would be an anthropologist who wanted to study a tribe without affecting their normal behavior in any way. True experiments , whilst giving analyzable data, often adversely influence the normal behavior of the subject. Descriptive research is often used as a pre-cursor to quantitative research designs, the general overview giving some valuable pointers as to what variables are worth testing quantitatively. Quantitative experiments are often expensive and time-consuming so it is often good sense to get an idea of what hypotheses are worth testing.
Because there are no variables manipulated , there is no way to statistically analyze the results. In addition, the results of observational studies are not repeatable , and so there can be no replication of the experiment and reviewing of the results. Descriptive research design is a valid method for researching specific subjects and as a precursor to more quantitative studies. Whilst there are some valid concerns about the statistical validity , as long as the limitations are understood by the researcher, this type of study is an invaluable scientific tool.
Whilst the results are always open to question and to different interpretations, there is no doubt that they are preferable to performing no research at all. Check out our quiz-page with tests about:. Martyn Shuttleworth Sep 26, Retrieved Sep 10, from Explorable. The text in this article is licensed under the Creative Commons-License Attribution 4. You can use it freely with some kind of link , and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations with clear attribution.
Case Study Research Design. 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:.
There are 8 scores and score 4 and 5 represent the halfway point. Since both of these scores are 20, the median is If the two middle scores had different values, you would have to interpolate to determine the median. The mode is the most frequently occurring value in the set of scores.
To determine the mode, you might again order the scores as shown above, and then count each one. The most frequently occurring value is the mode. In our example, the value 15 occurs three times and is the model. In some distributions there is more than one modal value. For instance, in a bimodal distribution there are two values that occur most frequently.
Notice that for the same set of 8 scores we got three different values -- If the distribution is truly normal i. Dispersion refers to the spread of the values around the central tendency. There are two common measures of dispersion, the range and the standard deviation.
The range is simply the highest value minus the lowest value. The Standard Deviation is a more accurate and detailed estimate of dispersion because an outlier can greatly exaggerate the range as was true in this example where the single outlier value of 36 stands apart from the rest of the values. The Standard Deviation shows the relation that set of scores has to the mean of the sample.
Again lets take the set of scores:. We know from above that the mean is So, the differences from the mean are:. Notice that values that are below the mean have negative discrepancies and values above it have positive ones. Next, we square each discrepancy:. Now, we take these "squares" and sum them to get the Sum of Squares SS value.
Here, the sum is Next, we divide this sum by the number of scores minus 1. Here, the result is This value is known as the variance. To get the standard deviation, we take the square root of the variance remember that we squared the deviations earlier.
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.
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 establish why it is that way or how it came to be” .
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. 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 or functions of person or group, the narration of facts, etc.
Many of the benefits and limitations of the specific descriptive research methods have been alluded to in previous modules in this series. Following is a summary regarding both the advantages and the disadvantages of using descriptive research methodology in general. Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.