A tabulation plan is a set of tabulation specifications, in which a research analyst outlines all the tables, statistics, and other special requests needed for analysis. The tabulation plan will serve as a guide for converting data into meaningful results.
What is the Meaning of Tabulation?
Tabulation refers to the system of processing data or information by organizing it into a table. With tabulation, numerical data is logically and systematically sorted into columns and rows, to facilitate statistical analysis.
The goal of tabulation is to present a large mass of complicated information in an orderly manner and allow viewers to draw reasonable conclusions and interpretations from it.
What are the Essential Parts of a Table?
To tabulate the data correctly, you have to know the eight essential parts of a table. They are as follows
Table number
It is the first part of a table and is indicated at the top of any table for easy identification and subsequent reference.
Table title
One of the most important parts of any table, its title is placed at the top of it and narrates its contents. It is imperative that the title is short, sharp and carefully written to describe the content of the tables effectively.
Header note
The header note of a table is presented in the part just below the title. It provides information about the data unit of the table, such as "quantity in rupees" or "quantity in kilograms", etc.
Column titles or subtitles
Titles are the part of the table above each column that explains the figures below each column.
Row headers or legends
The title of each horizontal row is called a heel.
Table body
It is the part that contains the numerical information collected from the facts investigated. Body data is presented in rows that are read horizontally from left to right and in columns, which are read vertically from top to bottom.
Footnote
The footnote is placed at the bottom of a table, above the source note, and is used to indicate any data that is not clear in the title, headings, legend, or footer of the table.
For example, if a table indicates the profit made by a company, a footnote can be used to indicate whether that profit is obtained before or after the tax calculation.
Source note
As the name suggests, a source note refers to the source from which the table information has been collected.
An illustration of the correct tabulation of the data
Below is a table to represent the total number of children in classes V, VI and VII at XYZ school.
Table number 1
Gender distribution of students in classes V, VI and VII of the XYZ School
Gender  V  SAW  VII  Total 
Children  50  60  65  175 
Girls  45  50  60  155 
Total  95  110  125  330 
Footnote. Source: XYZ School
This classification and tabulation of data facilitates comparison and statistical analysis and facilitates decisionmaking.
What are the objectives of tabulation?
Tabulation essentially serves as a bridge between data collection and analysis. The main objectives of tabulation can be summarized as follows
For the simplification of complex data
When any information is tabulated, the volume of raw data is compressed and presented in a much more simplified way. This makes it easier to understand and analyze previously complex data.
To highlight important information
Representing any data in the form of a table increases the chances of highlighting important information. Since the data is presented concisely without any textual explanation, any crucial information is automatically highlighted without difficulty.
For easy comparison
When data is presented in an orderly manner in rows and columns, it is easier to compare it based on several parameters. For example, it is easier to determine the month in which a country has received the maximum amount of rainfall if the data are presented in a table. Otherwise, there is always room for error in the correct treatment of the data.
Assist in the statistical analysis of data
Statistical analysis consists of calculating the correlation, mean, dispersion, etc. of the data. When information is presented in an organized way in a table, statistical analysis is much easier.
Save space
Although it may not seem as important as the other goal of tabulation, saving space without sacrificing data quality can be very useful in the long run. In addition, a table helps to present the facts much more concisely than page after page of text.
How do I run data tab?
Data tabulation can be done manually or with the help of a computer. In most cases, running the data tabulation depends on the cost, type and size of the study, computer availability, time available, and other factors.
If the tabulation is done on a computer, the answers are converted into numerical form. On the other hand, in the case of manual tabulation, the methods of lists, counting, sorting by cards and counting can be used.
These methods are explained as follows:
Direct count method
The codes are first noted on count sheets. A stroke is then marked against the codes to denote the response. After each fourth stroke code, the fifth response is given by putting a horizontal or diagonal line through the stroke.
Card sorting and counting method
This is perhaps the most effective manual tabulation method. Here the data is recorded on cards of various sizes and shapes with the help of a series of holes. The cards belonging to each of the categories are then separated and counted and their frequency is recorded. In this way, a total of 40 elements can be included on a single page.
List and count method
With this method, a large number of questionnaires are listed on a sheet. The answers to each question are entered in rows and the code corresponding to each question is represented in columns.
Tab types
In general, tabulation can be classified into two types: simple and complex tabulation.
Simple tab
It is the tabulation process by which information relating to one or more separate questions is illustrated. It is also known as oneway tabulation. Below is an example of this tab category 
Grades Obtained  Number of Students 
A+ (Over 80 points)  15 
A (7080)  20 
B (6070)  18 
C (5060)  25 
D (4050)  10 
Below 40  5 
Complex tabulation
They are the types of tables that represent the division of data into two or more categories based on two or more characteristics. This type of data tabulation can be divided into three types. These are:
Twoway tables
These tables illustrate the information gathered from two mutually dependent questions. For example, let's say a table has to illustrate the largest population in different states of India. This can be done in a oneway table. But if the population has to be compared in terms of the total number of men and women in each state, a twoway table will be required.
Threeway table
Like the category mentioned above, the threeway tables illustrate the information gathered from three mutually dependent and interrelated questions.
Let's take the example above and expand it with another category added to the table: the position of literacy between the male and female population of each state. The tabulation of these categories has to be put in a threeway table.
Multiple table
These tables are used to illustrate information gathered from more than three interrelated questions or characteristics.
Here are some examples:
Table 3. Anatomical location of nodules in the mammary gland
Hosp. "Calixto Garcia". 1994
Location 
No. of cases 
% 
Upper quadrants External Internal Lower quadrants External Internal Retroareolar Bilateral 
164 114 50 30 17 13 8 142 
47.7 33.2 14.5 8.7 4.9 3.8 2.3 41.3 
TOTAL 
344 
100.0 
Source: Medical Records
Table 4 Distribution by age groups according to the presence of breast disease
Hosp. "Calixto Garcia". 1994
Women examined 

Age groups 
With breast condition 
% 
No breast condition 
% 
Total 
% 
15 to 20 21 to 30 31 to 40 41 and over 
268 525 289 348

61.05 50.48 54.94 64.32 
171 516 237 193 
38.95 49.57 45.06 35.67 
439 1041 526 541

17.2 40.8 20.8 21.2 
Total 
1430 
56.14 
1117 
43.86 
2547 
100.0 
Source: Data obtained from the research
What are the tab rules?
There are some general rules that must be followed when building the tables. They are as follows:
Illustrated tables should be selfexplanatory. Although footnotes are part of the tables, they should not be mandatory to explain the meaning of the data presented in a table.
If the volume of information is considerable, it is better to put them in several tables instead of a single one. This reduces the chances of making mistakes and loses the goal of forming a table. However, each table formed must be complete in itself and used for analysis.
The number of rows and columns should be minimal to present the information clearly and concisely.
Before tabulating, the data should be approximate, whenever necessary.
Charts and legends should be selfexplanatory and should not require the help of footnotes for understanding.
If some positions of the collected data cannot be tabulated under any heel or heading, they should be noted in a separate table under the heading of several.
The quantity and quality of the data should in no case be compromised when forming a table.
Cross tabulation and chisquare
Pearson's chisquare or chisquare test is a statistical hypothesis that researchers use to determine whether there is a significant difference between the expected frequencies and those observed in one or more categories.
An important consideration when crosstabulating study results is to check whether the crosstabulation representation is true or false. This is similar to the doubt we have after entering a university, questioning whether this was really a good option or not.
To solve the dilemma, crosstabulation is calculated together with Chisquare analysis, which helps identify whether the study variables are independent or related to each other. If the two elements are independent, the tabulation is rated as insignificant, and the study would be rated as null hypothesis. As the factors are not related to each other, the result of the study is not reliable. Conversely, if there is a relationship between the two elements, that would confirm that the results of the tabulation are significant and can be trusted to make strategic decisions.
Cross Tabulation Results
An important consideration when crosstabulating study results is to check whether the crosstabulation representation is true or false. This is similar to the doubt we have after entering a university, questioning whether this was really a good option or not.
To solve the dilemma, crosstabulation is calculated together with Chisquare analysis, which helps identify whether the study variables are independent or related to each other. If the two elements are independent, the tabulation is rated as nonsignificant, and the study would be rated as null hypothesis. As the factors are not related to each other, the result of the study is not reliable. Conversely, if there is a relationship between the two elements, that would confirm that the results of the tabulation are significant and can be trusted to make strategic decisions.
Another important term we will introduce here is the null hypothesis. The null hypothesis assumes that any difference or importance observed in a dataset is by chance. The opposite of the null hypothesis is called the alternative hypothesis.
Application of Chi Square to Surveys
The application of chisquare to surveys is usually done with these types of questions:
Demographic
Likert Scale Questions
Towns
Product Name
Dates and number (when grouped)
For example, an engineer wants to determine how many defective parts were created on different production lines, during each shift. This table shows frequency counts for each production line and shift. Percentages and other table statistics can be used when analyzing the data.
Production line  Tomorrow  Night  Total 
to  4  25  29 
B  5  18  23 
C  3  23  26 
All  12  66  78 
As mentioned above, the chisquare test helps you determine whether two discrete variables are associated. If an association exists, the distribution of one of the variables will differ depending on the value of the second variable. But if the two variables are independent, the distribution of the first variable will be similar for all values of the second.
Applying the chisquare calculation to the above values  Pearson's chisquare = 0.803, Pvalue = 0.05. What does this mean? We have to pay attention to the pvalue. Compare the pvalue with its alpha level, which is usually 0.05.
If the pvalue is less than or equal to the alpha value, then the two variables are associated.
If the pvalue is greater than the alpha value, it is concluded that the variables are independent.
In this example, Pearson's chisquare statistic is 0.803 (with a pvalue of 0.05). Therefore, with an alpha value of 0.05, we conclude that there is no correlation and that it is insignificant.
Advantages of Cross Tabulation
An important advantage of using crosstabulation in a survey is that it is simple to calculate and very easy to understand. Even if the researcher does not have a deep knowledge of the concept, it is easy to interpret the results.
It eliminates confusion, as raw data can sometimes be difficult to understand and interpret. Even if these are small data sets, it is possible to get confused if the data is not arranged in an orderly fashion. Crosstabulation provides a simple way to correlate variables that helps minimize confusion related to data representation.
Crosstabulation allows you to obtain a large amount of data. As mentioned in the crosstabulation examples in the previous section, it is not easy to interpret the raw data. Crosstabulation traces the correlation between variables and makes it possible to clearly understand aspects that might otherwise have been overlooked. It is easy to understand perceptions even in a complicated form of statistics.
It provides qualified or relative data on two or more variables across multiple features with ease.
The most important advantage of using crosstabulation for survey analysis is the ease of using any data, whether nominal, ordinal, interval, or ratio.
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You may also be interested in: Quasi Experimental Design
Bibliographic References
Bhattacharyya, G. K., and R. A. Johnson, (1997). Statistical Concepts and Methods,John Wiley and Sons, New York.
Miller, R. G., Jr. (1981). Simultaneous Statistical Inference,SpringerVerlag, New York.
Scheffe, H. (1953). "A Method for Judging All Contrasts in the Analysis of Variance", Biometrika,40, pages 87104.