However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error. We also need to know that broadly sampling can be divided into two types — probability sampling and nonprobability sampling.
Examples of probability sampling include methods such as simple random sampling each member in a population has an equal chance of being selected , stratified random sampling in nonhomogeneous populations, the population is divided into subgroups — followed be random sampling in each subgroup , systematic sampling is based on a systematic technique — e. An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis.
A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources. We will first go through the sample size calculation for a hypothesis-based design like a randomized control trial.
The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance standard deviation for quantitative data , and expected proportions in the case of qualitative data.
This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size. It is also important to know whether the data follow a normal distribution or not. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error.
While there are no absolute rules, the minimal levels accepted are 0. For a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.
While we will not go into details of the formula for sample size calculation, some important points are as follows:. In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size.
What this means in effect is that reducing the effect size will lead to an increase in the required sample size. An increase in variance of the outcome leads to an increase in the calculated sample size.
This includes an idea about total population size this generally does not make a major difference when population size is above 20,, so in situations where population size is not known we can assume a population of 20, or more. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size.
In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. National Center for Biotechnology Information , U. Indian Dermatol Online J. Feroze Kaliyadan and Vinay Kulkarni 1. Author information Article notes Copyright and License information Disclaimer. Address for correspondence: Dr. E-mail: moc. Received Dec; Accepted Dec. This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.
Keywords: Biostatistics , descriptive statistics , sample size , variables. Variables What is a variable? Quantitative vs qualitative A variable can collect either qualitative or quantitative data.
Quantitative variables can be either discrete or continuous Discrete variables are variables in which no values may be assumed between the two given values e.
Dependent and independent variables In the context of an experimental study, the dependent variable also called outcome variable is directly linked to the primary outcome of the study. Descriptive Statistics Statistics can be broadly divided into descriptive statistics and inferential statistics.
Sorting and grouping Sorting and grouping is most commonly done using frequency distribution tables. Table 1 Stem and leaf plot. Open in a separate window.
Figure 1. Figure 2. Summary statistics The main tools used for summary statistics are broadly grouped into measures of central tendency such as mean, median, and mode and measures of dispersion or variation such as range, standard deviation, and variance. Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order: 30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86 Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data.
Mean The mean is basically the sum of all the values divided by the total number. Figure 3. Measures of dispersion The range gives the spread between the lowest and highest values.
The box plot The box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ]. Figure 4.
The concept of skewness and kurtosis Skewness is a measure of the symmetry of distribution. Figure 5. Figure 8. Respondents can also be affected by the presence of the observer and may engage in pretending. This is known as the observer effect. The researchers own opinions of biases may affect the results of the study. This is known as the experimenter effect. There is also the problem of representativeness , a case study or the data of a small sample does not adequately represent the whole population.
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We use cookies in our website to give you the best browsing experience and to tailor advertising. By continuing to use our website, you give us consent to the use of cookies. Descriptive Research : Definition, Method and Examples. Get ready to uncover the how, when, what, and where questions in a research problem.
Download Now. Share on facebook. Share on twitter. Share on linkedin. Table of Contents. What is descriptive research? Step by Step guide to Descriptive Research. Read More. What are the characteristics of descriptive research? Quantitative Research: Descriptive research is quantitative in nature as it attempts to collect information and statistically analyze it. See how data is collected via demographic survey template Nature of variables: The variables included in descriptive research are uncontrolled.
Cross-sectional studies: In descriptive research different sections of the same group are studied. Directs future research: Since descriptive research points out the patterns between variables and describes them, researchers can further study the data collected here.
Start Creating Descriptive Surveys Now. Start collecting insights at scale! What are the methods of descriptive research? Observational Method: All research has some component of observation , this observation can be quantitative or qualitative.
Uncover high quality insights with Voxco. Start uncovering high quality insights now! Survey Method: Survey method includes recording the answers of respondents through surveys or questionnaires. Case Study Method: The in-depth study of an individual or a group is known as a case study.
Sales Studies: Researchers can use descriptive research to analyze the potential of the market; what is currently trending in the market, which products have a chance of performing well in terms of sales. Consumer Perception and Behavior Studies: Researchers can use the methods of descriptive research to analyze the what consumers think about the brand, what are their perceptions understand them using our brand perception survey template about the products being sold by a particular brand and the uses of other competitive products.
Get deeper insights from customers and determine the best price for your offerings! See Pricing Options survey template Market Characteristic Studies: Another way in which researchers use descriptive research methods is by analyzing the distribution of the products in the market; which countries have more sales, which countries have fewer products but the product is sold out quickly.
Understand market characteristics with market segmentation survey template. Exploratory Research Guide. Conducting exploratory research seems tricky but an effective guide can help. What are the applications of descriptive research? To understand the objectives of research goals, here are some ways in which organization are using descriptive research: Defining the characteristics of respondents: Since most descriptive research methods use close-ended questions for the collection of data, it helps in drawing objective conclusions about the respondents.
Analyzing trends in data: With the help of statistical analysis that is provided by descriptive research methods, researchers are able to understand the trends in data overtime. Comparing different groups: Something closely knit to the previous point is also comparing different groups of customers based on their demographics.
Improve your survey research with our free demo! Validating existing patterns of respondents: Since descriptive research methods are non invasive and make the use of quantitative data mostly , researchers can make observations between why the current patterns of purchasing exist in customers.
Conducting research at different times Descriptive analysis can be conducted at different periods of times in order to see whether the patterns are similar or dissimilar at different points of time. Finding correlations amongst variables: Descriptive research methods are also used to draw correlations between variables and the degree of association between the variables. Book a Free Demo.
What are the advantages of descriptive research? What have been the reactions of school administrators to technological innovations in teaching the social sciences? How have high school computing courses changed over the last 10 years? How do the new multimediated textbooks compare to the print-based textbooks? How are decisions being made about using Channel One in schools, and for those schools that choose to use it, how is Channel One being implemented?
What is the best way to provide access to computer equipment in schools? How should instructional designers improve software design to make the software more appealing to students?
To what degree are special-education teachers well versed concerning assistive technology? Is there a relationship between experience with multimedia computers and problem-solving skills? How successful is a certain satellite-delivered Spanish course in terms of motivational value and academic achievement? Do teachers actually implement technology in the way they perceive? How many people use the AECT gopher server, and what do they use if for?
Descriptive research can be either quantitative or qualitative. It can involve collections of quantitative information that can be tabulated along a continuum in numerical form, such as scores on a test or the number of times a person chooses to use a-certain feature of a multimedia program, or it can describe categories of information such as gender or patterns of interaction when using technology in a group situation.
It often uses visual aids such as graphs and charts to aid the reader in understanding the data distribution. Because the human mind cannot extract the full import of a large mass of raw data, descriptive statistics are very important in reducing the data to manageable form.
When in-depth, narrative descriptions of small numbers of cases are involved, the research uses description as a tool to organize data into patterns that emerge during analysis. Those patterns aid the mind in comprehending a qualitative study and its implications. Most quantitative research falls into two areas: studies that describe events and studies aimed at discovering inferences or causal relationships. Studies of this type might describe the current state of multimedia usage in schools or patterns of activity resulting from group work at the computer.
An example of this is Cochenour, Hakes, and Neal's study of trends in compressed video applications with education and the private sector. Descriptive studies report summary data such as measures of central tendency including the mean, median, mode, deviance from the mean, variation, percentage, and correlation between variables. Survey research commonly includes that type of measurement, but often goes beyond the descriptive statistics in order to draw inferences.
See, for example, Signer's survey of computer-assisted instruction and at-risk students, or Nolan, McKinnon, and Soler's research on achieving equitable access to school computers. Thick, rich descriptions of phenomena can also emerge from qualitative studies, case studies, observational studies, interviews, and portfolio assessments. Robinson's case study of a televised news program in classrooms and Lee's case study about identifying values concerning school restructuring are excellent examples of case studies.
Descriptive research is unique in the number of variables employed. For example, a descriptive study might employ methods of analyzing correlations between multiple variables by using tests such as Pearson's Product Moment correlation, regression, or multiple regression analysis.
Good examples of this are the Knupfer and Hayes study about the effects of the Channel One broadcast on knowledge of current events, Manaev's study about mass media effectiveness, McKenna's study of the relationship between attributes of a radio program and it's appeal to listeners, Orey and Nelson's examination of learner interactions with hypermedia environments, and Shapiro's study of memory and decision processes.
On the other hand, descriptive research might simply report the percentage summary on a single variable. Examples of this are the tally of reference citations in selected instructional design and technology journals by Anglin and Towers ; Barry's investigation of the controversy surrounding advertising and Channel One; Lu, Morlan, Lerchlorlarn, Lee, and Dike's investigation of the international utilization of media in education ; and Pettersson, Metallinos, Muffoletto, Shaw, and Takakuwa's analysis of the use of verbo-visual information in teaching geography in various countries.
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