A tabulation plan is a set of tabulation specifications, in which a research analyst outlines all the tables, statistics, and other special requests necessary for his analysis. The tabulation plan will serve as a guide to convert the data into meaningful results.
What is the meaning of tabulation?
Tabulation refers to the data or information processing system by organizing it in a table. With tabulation, numerical data is arranged logically and systematically in columns and rows, to facilitate statistical analysis.
The goal of tabulation is to present a large mass of complicated information in an orderly fashion and to allow viewers to draw reasonable conclusions and interpretations from it.
What are the Essential Parts of a Table?
To tabulate data correctly, you need to know the eight essential parts of a table. They are the following
table number
It is the first part of a table and is indicated at the top of any table for easy identification and later reference.
table title
One of the most important parts of any table, its title is placed at the top of it and narrates its content. It is essential that the title is short, clear and carefully written to describe the content of the tables effectively.
header note
The heading note of a table is presented in the part just below the title. Provides information about the data unit of the table, such as “amount in rupees” or “amount in kilograms”, etc.
Column headings or subheadings
The headings 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 stub.
table body
It is the part that contains the numerical information collected from the facts investigated. Body data is presented in rows, which read horizontally from left to right, and in columns, which 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 anything that is not clear from 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 made before or after the calculation of taxes.
source note
As its name suggests, a source note refers to the source from which the information in the table was collected.
An illustration of the correct tabulation of data
A table is illustrated below to represent the total number of boys and girls in classes V, VI, and VII at school XYZ.
Table Number 1
Distribution by sex of the students of classes V, VI and VII of the XYZ School
| Gender | v | SAW | 7th | Total |
| Kids | fifty | 60 | 65 | 175 |
| girls | Four. Five | fifty | 60 | 155 |
| Total | 95 | 110 | 125 | 330 |
Footnote. Source: XYZ School
This classification and tabulation of data facilitates comparison and statistical analysis and facilitates decision making.
What are the goals of tabulation?
The tabulation essentially serves as a bridge between data collection and analysis. The main objectives of the tabulation can be summarized as follows
For simplifying 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 tabular form 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 neatly in rows and columns, it is easier to compare data based on various parameters. For example, it is easier to determine the month in which a country has received the maximum amount of rainfall if the data is presented in a table. Otherwise, there is always room for mistakes in the correct treatment of the data.
Help statistical analysis of data
Statistical analysis consists of calculating the correlation, the mean, the dispersion, etc. of the data. When the 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. Also, a table helps to present the facts much more concisely than page after page of text.
How is data tabulation executed?
Data tabulation can be done manually or with the help of a computer. In most cases, the execution of the data tabulation depends on the cost, the type and size of the study, the availability of computers, the time available, and other factors.
If the tabulation is done on a computer, the answers are converted into numerical form. Instead, in the case of manual tabulation, the list, count, card sort, and count methods can be used.
These methods are explained as follows:
Direct counting method
The codes are first noted on tally sheets. A trace is then drawn against the codes to denote the response. After every fourth stroke code, the fifth answer is given by putting a horizontal or diagonal line across the stroke.
Card sorting and counting method
This is perhaps the most efficient manual tabulation method. Here the data is recorded on cards of various sizes and shapes with the help of a series of holes. Next, the cards belonging to each of the categories are 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 one 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 tabulation
It is the tabulation process by which the information related to one or more independent questions is illustrated. Also known as one-way tabulation. Here is an example of this tabulation category –
| Scores Earned | Number of students |
| A+ (Over 80 points) | fifteen |
| A (70-80) | twenty |
| B(60-70) | 18 |
| C(50-60) | 25 |
| D (40-50) | 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:
two way tables
These tables illustrate the information collected 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 one way table. But if the population is to be compared in terms of the total number of men and women in each state, a two-way table will be required.
three way table
Like the category mentioned above, three-way tables illustrate information collected from three mutually dependent and interrelated questions.
Let’s take the previous example and expand it with another category added to the table: the position of literacy among the male and female population of each state. The tabulation of these categories has to be put into a three-way table.
multiple table
These tables are used to illustrate information collected from more than three interrelated questions or characteristics.
Here are some examples:
Table 3. Anatomical location of the nodules in the mammary gland.
Hospital Calixto Garcia. 1994
| Location | No of cases | % |
| upper quadrants External Internal lower quadrants External Internal retroareolar Bilateral | 164 114 fifty 30 17 13 8 142 | 47.7 33.2 14.5 8.7 4.9 3.8 23 41.3 |
| TOTAL | 344 | 100.0 |
Source: Clinical Histories
Table 4 Distribution by age groups according to the presence of breast disease
Hospital 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 investigation
What are the tabulation rules?
There are some general rules that should be followed when building the tables. They are the following:
Illustrated tables should be self-explanatory. Although footnotes are part of the tables, they should not be required 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 just one. This reduces the chances of making mistakes and misses the point of forming a table. However, each table formed must be complete in itself and serve for analysis.
The number of rows and columns should be kept to a minimum to present the information clearly and concisely.
Before tabulating, the data should be approximated, whenever necessary.
Tables and legends should be self-explanatory and should not require the aid of footnotes for understanding.
If some positions of the collected data cannot be tabulated under any tab or heading, they should be noted in a separate table under the heading of various.
The quantity and quality of the data must never be compromised when forming a table.
Cross tabulation and chi-square
The chi-square or Pearson’s chi-square test is a statistical hypothesis that researchers use to determine if there is a significant difference between the expected and observed frequencies in one or more categories.
An important consideration when cross-tabulating study results is to verify whether the cross-tabulated representation is true or false. This is similar to the doubt we have after entering a university, questioning whether it was really a good option or not.
To solve the dilemma, the cross tabulation is calculated together with the Chi-square analysis, which helps to identify whether the study variables are independent or related to each other. If the two items are independent, the tabulation is scored as insignificant, and the study would be scored as a 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 items, that would confirm that the tabulation results are meaningful and can be relied upon to make strategic decisions.
Cross Tabulation Results
An important consideration when cross-tabulating study results is to verify whether the cross-tabulated representation is true or false. This is similar to the doubt we have after entering a university, questioning whether it was really a good option or not.
To solve the dilemma, the cross tabulation is calculated together with the Chi-square analysis, which helps to identify whether the study variables are independent or related to each other. If the two items are independent, the tabulation is scored as not significant, and the study would be scored 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 items, that would confirm that the tabulation results are meaningful and can be relied upon to make strategic decisions.
Another important term that we will introduce here is the null hypothesis. The null hypothesis assumes that any difference or significance observed in a data set is due to chance. The opposite of the null hypothesis is called the alternative hypothesis.
Application of Chi Square to Surveys
The application of the chi-square to surveys is usually done with these types of questions:
demographics
Likert scale questions
cities
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 | morning | Night | Total |
| A | 4 | 25 | 29 |
| B. | 5 | 18 | 23 |
| C | 3 | 23 | 26 |
| All | 12 | 66 | 78 |
As mentioned above, the chi-square test helps you determine if two discrete variables are associated. If there is an association, 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 chi-square calculation to the above values – Pearson’s chi-square= 0.803, P-value= 0.05. What does this mean? We have to pay attention to the p-value. Compare the p-value to your alpha level, which is typically 0.05.
If the p-value is less than or equal to the alpha value, then the two variables are associated.
If the p-value is greater than the alpha value, the variables are concluded to be independent.
In this example, the Pearson chi-square statistic is 0.803 (with a p-value of 0.05). Therefore, with an alpha value of 0.05, we conclude that there is no correlation and that it is negligible.
Advantages of Cross Tabulation
An important advantage of using cross tabulation in a survey is that it is simple to calculate and very easy to understand. Even if the researcher does not have a deep understanding of the concept, it is easy to interpret the results.
Eliminate confusion, as raw data can sometimes be difficult to understand and interpret. Even with small data sets, it’s possible to get confused if the data isn’t arranged in an orderly fashion. Crosstabulation offers an easy way to correlate variables that helps minimize confusion related to data representation.
Cross tabulation allows to obtain numerous data. As mentioned in the crosstabulation examples in the previous section, it is not easy to interpret the raw data. Cross-tabulation traces the correlation between variables and provides a clear understanding of aspects that might otherwise have been overlooked. Perceptions are easy to understand even in a complicated form of statistics.
Provide qualified or relative data on two or more variables across multiple features with ease.
The most important advantage of using cross tabulation for survey analysis is the ease of using any data, whether nominal, ordinal, interval, or ratio.
