If you want to stay out of difficulties you can select all the types of your material from only one row of the table below. The situation is different in normative research, i. When a scientist today starts selecting tools for analysis in a new research project, almost inevitably the computer comes first in mind. Indeed, modern computers are powerful tools for analysis, but you have to remember that they have severe restrictions, too.
What the machine demands, above all, is that the material it receives be suitable for electronic manipulations. For almost any type of information there are specific computer programs which can store and manipulate exactly that genre of information, but usually no other types of material.
What is specially disappointing to a researcher, is that programs quite often refuse to analyze relations between different types of information, or they accept just some sorts of relationships for analysis, others not. Usual classes of information that you will deal with in the research and development of products include: Measurements, in other words, quantitative study Spreadsheet programs like Excel.
The researcher must first "operationalize" all the factors, making them measurable. Classifications presented often as codes or tabulations Spreadsheet or data-base programs. Classification can handle all types of data, but their relationships only superficially.
Verbal written or spoken information, in other words, qualitative study. Word processing does seldom help doing analysis, but it is excellent for reporting its results as text with illustrations. Other mental patterns like attitudes and preferences. Choice of computer program depends on the language which you use when making explicit the tacit information. Usually it will be one of the first three above. There are many computer programs for storing and manipulating images, but their abilities in analysis are poor.
Make the analysis manually and report it as text with illustrations. Patterns of action, e. See also Developing an Activity Make the analysis manually and report it as text with illustrations. As the table above already indicates, it quite often happens that you will find no computer program that could handle all the types of data that you want to analyze.
In such a situation you should consider if you can " operationalize " or transform the inconvenient class of your material into one of those formats that your program of analysis can handle.
This operation, which you normally have to do manually, means for example Quantifying: Once you have transformed most or all of your data into measurable variables you can feed your material into a spreadsheet program like Excel, or into a statistics program. These programs quickly tell you if the relationships between your variables follow any known mathematical pattern, see Quantitative Analysis. After this operation you can feed your material into a data base program which contain a multitude of sorting and logical operations which can be used to uncover the hidden structures in the data.
Finally, do not forget the alternative of analyzing your data without a computer. Methods which work nearly always, include the following: Cross-tabulate all the material manually on a large sheet of paper, perhaps with the help of card files, copying machine and folders. This is a venerable method that is still effective in the case that you need analyze relations between not more than two or three properties of the studied cases.
If you need more than three or four dimensions in the table it becomes difficult to discern the relationships. Qualitative analysis can include all kinds of material, even measurements and pictures etc. The principal tool will be your brain and the report will be verbal.
It works well in a case study, but the clarity of analysis tends to suffer if you need to analyze more than a handful of cases. When contemplating the merits and weaknesses of various methods and tools the crucial point is certainly their ability in uncovering the hidden relationships, the invariances in the source data. Nevertheless you should not forget that the analysis tool must also present the findings that became revealed during the analysis.
Many computer programs can present their results as beautiful logical models like graphs, trees or tables. However, if the analysis program is unable to produce graphics, or if you could not use a computer for the job at all, you perhaps can present the final results as diagrams made by hand or you can use a specific drawing program.
Some word-processing programs also include simple drawing tools. After the analysis phase most research projects include an important procedure: Quantitative statistics programs contain specific tools for this task. For qualitative analysis such tools do not exist, and the assessment is mostly done by contemplating standard lists of certain critical questions, some of which are presented elsewhere under the titles Assessing the Outcome of Literature Study , Assessing Qualitative Observations and Assessing Theoretical Output.
Some research projects include still special procedures like forecasting the future development of the object of study or developing an activity or an industrial product. Available tools for them are discussed on separate www-pages. The final task in a research project, reporting , necessitates a word processing or publishing program because you will usually want to complement the results of analysis with lengthy verbal comments and explanations, and you will then need powerful tools for layout, cross-referencing, indexing, making the table of contents etc.
Modern publishing programs accept the graphics produced by the usual analysis programs; if not, you can insert the illustrations eventually by hand. Intensive study of a few cases which are often studied holistically, noting all their characteristics:.
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.
This would be SQRT Although this computation may seem convoluted, it's actually quite simple. To see this, consider the formula for the standard deviation:. In the top part of the ratio, the numerator, we see that each score has the the mean subtracted from it, the difference is squared, and the squares are summed. In the bottom part, we take the number of scores minus 1. The ratio is the variance and the square root is the standard deviation.
In English, we can describe the standard deviation as:. Although we can calculate these univariate statistics by hand, it gets quite tedious when you have more than a few values and variables. Every statistics program is capable of calculating them easily for you. The standard deviation allows us to reach some conclusions about specific scores in our distribution. Assuming that the distribution of scores is normal or bell-shaped or close to it!
Research Methods William G. Zikmund Basic Data Analysis: Descriptive Statistics Health Economics Research Method /2 Descriptive Analysis • The transformation of raw data into a form that will make them easy to understand and interpret; rearranging, ordering, and manipulating data to generate descriptive.
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.
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. Descriptive analysis in education: A guide for researchers Descriptive Analysis and the Scientific Method Descriptive An cated throughout the education and research communities. Descriptive analysis characterizes the world or a phenomenon— identifying patterns in.
Methods for the descriptive analysis and systematic review of effectiveness - Adaptive E-Learning to Improve Dietary Behaviour: A Systematic Review and Cost-Effectiveness Analysis PubMed Health Your browsing activity is empty. 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.