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Data Analysis
Frequency Counts
From data Master Sheet, simple tables can be made with Frequency Counts for each variable. A frequency count is an enumeration of how often a certain measurement or a certain answer to a specific question occurs.
For Example,
Smokers……… 63
Nonsmokers…..74
Total………….137
If numbers are large enough it is better to calculate the frequency distribution in percentages (relative frequency). This makes it easier to compare groups than when absolute numbers are given. In other words, percentages standardize the data.
A percentage is the number of units in the sample with a certain characteristic, divided by the total number of units in the sample and multiplied by 100
In the above example the calculation of the percentage answers the question. If I had asked 100 People who answered yes to smoking which if shown in percentage instead of absolute values then I have to calculate like this
Total responses of smoking yes divided by total sample number of elements multiplied by 100 e.g.
63/137x100 =46 %
After doing these calculations of relative frequency, you can present your data, in a frequency table shown below
Number of smokers and nonsmokers in the sample
“Don’t know” is not to be taken as a nonresponse. If applicable, a category “Don’t know” should appear in the data master sheet and in frequency table.
It is usually necessary to summarize the data from numerical variable by dividing them into categories; this process includes the following steps
Cross Tabulation
In addition to making frequency counts for one variable at a time, it may be useful to combine information on two or more variables to describe the problem or to arrive t possible explanation for it.
For this purpose it is necessary to design CROSS-TABULATIONS.
Depending on the objectives and type of study, three different kinds of
Cross Tabulations may be required
1. Descriptive cross-tabulation, which aim at describing the problem under study;
2. Analytic cross-tabulation, in which groups are compared to determine to determine differences; and
3. Analytic cross-tabulations, which focus on exploring relationships between variables.
When the plan for data analysis is being developed, the data are, of course, not yet available. However to visualize how the data can be organized and summarized, it is useful at this stage to construct the so-called DUMMY
Cross- Tabulation
A Dummy Table contains all elements of real table, except that the cells are not yet filled with the awaited data.
When the data is available then fill the cells and the Dummy Table will be converted into a real table.
Making Dummy Table will help you in many ways like for example.
There are two different ways to handle the data when doing the tallying. Either master sheet can be used or the original questionnaires can be sorted by hand
In the latter case you would go through the following steps
1. Divide the forms into two piles, one for smokers and one for nonsmokers
2. divide each pile into one for those without cough and one for those who had an episode of cough (we now have four piles)
3. count the number in each pile and fill in the table
Some Hints for constructing tables:
If a dependant and independent variable are cross-tabulated, the independent variable is usually placed vertically (at the left side of the table in a column) and the dependant variable horizontally along the top of the table.
All tables should have a clear title and clear heading for all columns and rows
All tables should have separate columns and rows for totals to enable you to check if your totals are the same for all variables and to make further analysis easier.
Frequency Counts
From data Master Sheet, simple tables can be made with Frequency Counts for each variable. A frequency count is an enumeration of how often a certain measurement or a certain answer to a specific question occurs.
For Example,
Smokers……… 63
Nonsmokers…..74
Total………….137
If numbers are large enough it is better to calculate the frequency distribution in percentages (relative frequency). This makes it easier to compare groups than when absolute numbers are given. In other words, percentages standardize the data.
A percentage is the number of units in the sample with a certain characteristic, divided by the total number of units in the sample and multiplied by 100
In the above example the calculation of the percentage answers the question. If I had asked 100 People who answered yes to smoking which if shown in percentage instead of absolute values then I have to calculate like this
Total responses of smoking yes divided by total sample number of elements multiplied by 100 e.g.
63/137x100 =46 %
After doing these calculations of relative frequency, you can present your data, in a frequency table shown below
Number of smokers and nonsmokers in the sample
Category | Frequency | Relative Frequency |
Nonsmokers | 63 | 46 % |
Smokers | 74 | 54 % |
Total | 137 | 100 % |
“Don’t know” is not to be taken as a nonresponse. If applicable, a category “Don’t know” should appear in the data master sheet and in frequency table.
It is usually necessary to summarize the data from numerical variable by dividing them into categories; this process includes the following steps
- Inspect the figures; what is their range? (The difference between the largest and the smallest measurement)
- Divide the range into 3 to 5 categories. You can either aim at having reasonable number in each category (e.g. 0-2 km,3-4, 5-9 and 10+ km for home to health centre distance), or you can define the categories in such a way that they all start with round number (e.g. 20 to 29 , 30 to 39 ,40 to 49 years etc)
- construct a table indicating how data are grouped and count the observations in each group
Cross Tabulation
In addition to making frequency counts for one variable at a time, it may be useful to combine information on two or more variables to describe the problem or to arrive t possible explanation for it.
For this purpose it is necessary to design CROSS-TABULATIONS.
Depending on the objectives and type of study, three different kinds of
Cross Tabulations may be required
1. Descriptive cross-tabulation, which aim at describing the problem under study;
2. Analytic cross-tabulation, in which groups are compared to determine to determine differences; and
3. Analytic cross-tabulations, which focus on exploring relationships between variables.
When the plan for data analysis is being developed, the data are, of course, not yet available. However to visualize how the data can be organized and summarized, it is useful at this stage to construct the so-called DUMMY
Cross- Tabulation
A Dummy Table contains all elements of real table, except that the cells are not yet filled with the awaited data.
Cough in last 2 days | No Cough in last 2 days | Total | |
Smokers | |||
Nonsmokers | |||
Total |
When the data is available then fill the cells and the Dummy Table will be converted into a real table.
Making Dummy Table will help you in many ways like for example.
- It will save your time when the data is arrived
- Will give you another chance to have a glance on your objective, and hence will be refined further
- Dummy Table will prevent you from collecting too little or too much data
- will assist you in the possible explanation for the problem you have identified
There are two different ways to handle the data when doing the tallying. Either master sheet can be used or the original questionnaires can be sorted by hand
In the latter case you would go through the following steps
1. Divide the forms into two piles, one for smokers and one for nonsmokers
2. divide each pile into one for those without cough and one for those who had an episode of cough (we now have four piles)
3. count the number in each pile and fill in the table
Some Hints for constructing tables:
If a dependant and independent variable are cross-tabulated, the independent variable is usually placed vertically (at the left side of the table in a column) and the dependant variable horizontally along the top of the table.
All tables should have a clear title and clear heading for all columns and rows
All tables should have separate columns and rows for totals to enable you to check if your totals are the same for all variables and to make further analysis easier.
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