Data-Processing Methods

Data processing’s total task in carrying out the analytical program is this: to convert crude fragments of observations and responses that we just coded into orderly statistics that are ready of interpretation. Methods of processing data can be placed into two types: manual and computer. Electronic methods other than computers do exist but no longer have sufficient usage to be mentioned. The methods possess unique advantages and disadvantages, and a brief discussion of each will enable you to grasp the implications fo using a particular method.

Manual methods can be divided into two types. One of these, tallying, is completely by hand, entering the responses in appropriate categories on worksheets. In this simple method, the ‘sorting’ is done individually for each observation by selecting the line on which to tally it. Tallying tends to be done more accurately by having two persons work on it, one calling off the responses while the other tallies.

The sort-and-count method is exactly that: first, sort all questionnaires or data forms into piles, one for each answer category; then, count each pile. This avoids the tallying danger of making entries on the wrong line and can be speedier, provided that it is easy to read and sort the entries for all questions and categories. A variant of sort-and-count is keysort, a copyrighted name of Litton Industries that uses a standard card that can be sorted and counted manually with simple equipment. Along the edges of this card are rows of holes that may be designated as fields and given code numbers. Then, at appropriate places for the observed data, the margin is punched to make a notch. When all the cards are notched and assembled so that the holes are in line, a rod is inserted through the hole representing the data category being counted. When raised with this needle, the cards punched at the hole will fall; then they are counted. Keysort is a quicker and more accurate method.

Computer Processing

The use of computers in data processing has increased at a fantastic rate during the past decade. On account of the rapid use of computers in varied spheres it is said that human civilization has entered the second industrial revolution. “The first industrial revolution freed man’s muscles; the second is freeing his mind for more challenging pursuits.” However, processing of data by computers may not always be economically beneficial. It is, therefore, necessary to know when computers should be used.

In the processing of data, computers may be used most efficiently if the processing operations have one or more of the following characteristics.

• Large Volume of Input: When a large amount of data needs to be processed, computer processing may turn out to be more economical as compared to other methods of data processing.
• Repetition of Projects: When repetitive projects are undertaken by an organization, the use of the computer for processing data is advisable. In such cases too, computer processing will generally be cheaper than its use in individual projects.
• Desired and Necessary Greater Speed in Processing: When information is needed in a limited time, manual processing of data should be given up in favor of computer processing because of its unbeatable speed.
• Desired and Necessary Greater Accuracy: There are likely to be mistakes in data processing by manual methods. Computer processing will be more accurate provided sufficient care has been exercised in planning the task.
• Processing Complexities that require Electronic Help: When a number of interacting variables are involved in the data, computer will be most appropriate. Certain analytical tools such as linear programming, business simulation, factor analysis and discriminant analysis generally require the use of a computer.

Figure 12.3 shows the average cost relationships of the different methods of data processing.

Annual Processing Volume

Fig. 12.3 Average Cost Relationship of Different methods of Data Processing.

(Source: Sanders, Donald H., Computers in Business: An Introduction, Tokyo, McGraw-Hill Kogakusha Ltd. 1975. Reprinted with the kind permission of McGraw-Hill Book Company, New York.)

The diagram shows that at point A there is a break-even cost position between manual and computer processing of A’ volume of data. Beyond this volume, if data are to be processed, the use of the computer will be more economical. Likewise, points B and C are other break even points. Point B shows the break-even cost position between machine assisted manual processing and computer processing. Thus, it may be more economical to use the machine-assisted manual method of data processing when the volume of data to be processed is up to B’. the use of computers for data upto B’ volume will be costlier. Beyond this point, the computer is more economical. However, when the volume of data is beyond B’ but up to C’, we find that another method of data processing, namely, that of punched cards turns out to be more economical than computer processing. In other words, the computer should be used when the volume of data to be processed is quite large, in this case beyond C’.

It may be noted that these average cost curves are not stable and they frequently change on account of changes in clerical labour rates and the cost of clerical office supplies. Over the years, there have been innovations in computer hardware. Such innovations have shifted the computer cost curse downward. These shifts in the cost curves have, on the whole, made computer processing more attractive for ware volumes of data processing. In general, the suitability of methods can be judged on the basis of the volume of data processing.

Data Analysis and Interpretation: Analysis of data is the process by which data is converted into useful information. Raw data is collected from questionnaire cannot be used unless it is processed in some way to make it amenable to drawing conclusions. Various techniques of data analysis are available, and it is sometimes difficult to choose one that will be the most appropriate for the research problem on hand. Analysis should be planned at the time of designing the questionnaire , as special kind of analysis are needed, requiring specific forms or scale of data.