Prepared by : Savan Modhavaniya Reg. No. : WRO0720928 Submitted to : Rajkot Branch of ICAI ( ICAI Bhawan, Rajkot. ). 

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Prepared by : Savan  Modhavaniya Reg. No. : WRO0720928 Submitted to : Rajkot Branch of ICAI ( ICAI Bhawan, Rajkot. )

Introduction Terminology When to use stratifications ? Stratifications analysis consideration Stratifications procedure Stratified Sampling How to form strata ? Disproportionate Stratified Sampling Proportionate Stratified Sampling Advantages Disadvantages Stratifications using IDEA software. 

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Introduction Terminology When to use stratifications ? Stratifications analysis consideration Stratifications procedure Stratified Sampling How to form strata ?  Disproportionate Stratified Sampling Proportionate Stratified Sampling Advantages Disadvantages Stratifications using IDEA software

Stratification is defined as the act of sorting data, people, and objects into distinct groups or layers. It is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together , the meaning of the data can be difficult to see.. 

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Stratification is defined as the act of sorting data, people, and objects into distinct groups or layers. It is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together , the meaning of the data can be difficult to see.

POPULATION : It consist of the totality or aggregate of the observation with which the researcher is concerned. SAMPLING – Process of choosing a representative portion of the entire population. TARGET POPULATION : It is a group of things which meets the certain criteria. STRATUM : It refers to a subset (part) of the population (entire collection of items under consideration) which is being sampled.. 

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POPULATION  : It consist of the totality or aggregate of the observation with which the researcher is concerned. SAMPLING  – Process of choosing a representative portion of the entire population. TARGET POPULATION  : It is a group of things which meets the certain criteria. STRATUM  : It refers to a subset (part) of the population (entire collection of items under consideration) which is being sampled.

POPULATION Sample. Example of sample. 100 Students are required for study. The population of ABC students is 600 , only 200 ABC students are included as the target population and only 100 students are chosen as samples for the actual study.. 

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100  Students are required for study. The population of ABC students is  600 , only  200  ABC students are included as the target population and only  100  students are chosen as samples for the actual study.

When to use Stratification ?. Before collecting data. When data come from several sources or conditions, such as shifts, days of the week, suppliers, or population groups. When data analysis may require separating different sources or conditions.. 

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Before collecting data.  When data come from several sources or conditions, such as shifts, days of the week, suppliers, or population groups. When data analysis may require separating different sources or conditions.

Survey data usually benefit from stratification. Always consider before collecting data whether stratification might be needed during analysis. Plan to collect stratification information. On your graph or chart, include a legend that identifies the marks or colours used .. 

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Survey data usually benefit from stratification. Always consider before collecting data whether stratification might be needed during analysis. Plan to collect stratification information. On your graph or chart, include a legend that identifies the marks or colours used .

Before collecting data, consider which information about the sources of the data might have an effect on the results. Set up the data collection so that you collect that information as well. When plotting or graphing the collected data on a scatter diagram, control chart, histogram, or other analysis tool, use different marks or colours to distinguish data from various sources. Data that are distinguished in this way are said to be “stratified.” Analyse the subsets of stratified data separately.. 

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Before collecting data, consider which information about the sources of the data might have an effect on the results. Set up the data collection so that you collect that information as well. When plotting or graphing the collected data on a scatter diagram, control chart, histogram, or other analysis tool, use different marks or colours to distinguish data from various sources. Data that are distinguished in this way are said to be “stratified.” Analyse the subsets of stratified data separately.

STRATIFICATION : The elements in the population are divided into the layer/group/strata based on their values. Strata must be non-overlapping and together constitute the whole population. SAMPLING WITHIN THE STRATA : Samples are selected independently from each stratum. Different selection method can be used In different strata.. 

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STRATIFICATION  : The elements in the population are divided into the layer/group/strata based on their values. Strata must be non-overlapping and together constitute the whole population.   SAMPLING WITHIN THE STRATA  : Samples are selected independently from each stratum. Different selection method can be used In different strata.

Stratified Randon• Sarnp,tlnz Group SRS SRS Four Graphic breakdown of stratified randorn sannpling. 

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Stratified Randon• Sarnp,tlnz
Group
SRS
SRS
Four
Graphic breakdown of stratified
randorn sannpling

As we can say that the strata Can be formed on the basis of common Characteristics Of the item in the Each stratum. Strata are usually Based on the past experience and judgement of the researchers. We can take small samples of equal size from the proposed strata and then examine the variances among the possible stratifications.. 

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As we can say that the strata  Can be formed on the basis of common Characteristics Of the item in the Each stratum. Strata are usually Based on the past experience and judgement of the researchers. We can take small samples of equal size from the proposed strata and then examine the variances among the possible stratifications.

Disproportionate stratified sampling is a stratified sampling method where the sample population is not proportional to the distribution within the population of interest. The implication is that the members of different subgroups do not have an equal opportunity to be a part of the research sample.. 

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Disproportionate Stratified Sampling Method

Disproportionate stratified sampling is a stratified sampling method where the sample population is not proportional to the distribution within the population of interest. The implication is that the members of different subgroups do not have an equal opportunity to be a part of the research sample.

A researcher splits the population of interest into three subsets based on their age groups: Subset A (16–25): 120,000 Subset B (26–35): 80,000 Subset C (36–45): 100,000 Disproportionate stratified sampling means the researcher randomly chooses members of the sample from each group. So, you could have 60,000 participants from the first group and 20,000 and 17,000 from others, respectively. There’s no clear-cut method for choosing the variables for the research sample .. 

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Example of Disproportionate Stratified Sampling

A researcher splits the population of interest into three subsets based on their age groups: 
Subset A (16–25): 120,000 Subset B (26–35): 80,000 
Subset C (36–45): 100,000  
Disproportionate stratified sampling means the researcher randomly chooses members of the sample from each group. So, you could have 60,000 participants from the first group and 20,000 and 17,000 from others, respectively. There’s no clear-cut method for choosing the variables for the research sample .

In proportionate stratified sampling, the researcher selects variables for the sample based on their original distribution in the population of interest. This means that the probability of choosing a variable from a stratum for the sample depends on the relative size of the stratum in its population of interest. Typically, the researcher derives a sampling fraction and uses this fraction to determine how the variables are selected for the sample. This sampling fraction is always the same across all strata, regardless of their sizes. With disproportionate stratified sampling, every unit in a stratum stands the same chance of getting selected for the systematic investigation.. 

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In proportionate stratified sampling, the researcher selects variables for the sample based on their original distribution in the population of interest. This means that the probability of choosing a variable from a stratum for the sample depends on the relative size of the stratum in its population of interest.  Typically, the researcher derives a sampling fraction and uses this fraction to determine how the variables are selected for the sample. This sampling fraction is always the same across all strata, regardless of their sizes. With disproportionate stratified sampling, every unit in a stratum stands the same chance of getting selected for the systematic investigation.

As part of a research to know how many students want to pursue a career in the sciences. First, she splits the population of interest into two strata based on gender so that we have 4,000 male students and 6,000 female students. Next, she uses ⅕ as her sampling fraction and selects 800 male students and 1,200 female students for the sample population.. 

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Example of Proportionate Stratified Sampling

As part of a research to know how many students want to pursue a career in the sciences. First, she splits the population of interest into two strata based on gender so that we have 4,000 male students and 6,000 female students. 
Next, she uses ⅕ as her sampling fraction and selects 800 male students and 1,200 female students for the sample population.

If measurements within strata have lower standard deviation (as compared to the overall standard deviation in the population), stratification gives smaller error in estimation.
For many applications, measurements become more manageable and/or cheaper when the population is grouped into strata.
When it is desirable to have estimates of population parameters for groups within the population – stratified sampling verifies we have enough samples from the strata of interest.. 

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If measurements within strata have lower standard deviation (as compared to the overall standard deviation in the population), stratification gives smaller error in estimation.
For many applications, measurements become more manageable and/or cheaper when the population is grouped into strata.
When it is desirable to have estimates of population parameters for groups within the population – stratified sampling verifies we have enough samples from the strata of interest.

Better accuracy in results in comparison to other probability sampling methods such as cluster sampling, simple random sampling, and systematic sampling or non-probability methods such as convenience sampling. This accuracy will be dependent on the distinction of various strata, i.e. Results will be highly accurate if all the strata are extremely different.. 

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Better accuracy in results in comparison to other probability sampling methods such as cluster sampling, simple random sampling, and systematic sampling or non-probability methods such as convenience sampling. This accuracy will be dependent on the distinction of various strata, i.e. Results will be highly accurate if all the strata are extremely different.

Convenient to train a team to stratify a sample due to the exactness of the nature of this Sampling technique. Due to statistical accuracy of this method, smaller sample size can also retrieve highly useful results for a researcher. This Sampling technique covers maximum population as the researchers have complete charge over the strata division.. 

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Convenient to train a team to stratify a sample due to the exactness of the nature of this Sampling technique. Due to statistical accuracy of this method, smaller sample size can also retrieve highly useful results for a researcher. This Sampling technique covers maximum population as the researchers have complete charge over the strata division.

Stratified sampling is not useful when the population cannot be exhaustively partitioned into disjoint subgroups. It would be a misapplication of the technique to make subgroups' sample sizes proportional to the amount of data available from the subgroups, rather than scaling sample sizes to subgroup sizes. Data representing each subgroup are taken to be of equal importance if suspected variation among them warrants stratified sampling.. 

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Stratified sampling is not useful when the population cannot be exhaustively partitioned into disjoint subgroups. It would be a misapplication of the technique to make subgroups' sample sizes proportional to the amount of data available from the subgroups, rather than scaling sample sizes to subgroup  sizes. Data representing each subgroup are taken to be of equal importance if suspected variation among them warrants stratified sampling.

Continue . . .. If subgroup variances differ significantly and the data needs to be stratified by variance, it is not possible to simultaneously make each subgroup sample size proportional to subgroup size within the total population. The problem of stratified sampling in the case of unknown class priors (ratio of subpopulations in the entire population) can have deleterious effect on the performance of any analysis on the dataset. 

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If subgroup variances differ significantly and the data needs to be stratified by variance, it is not possible to simultaneously make each subgroup sample size proportional to subgroup size within the total population. The problem of stratified sampling in the case of unknown class priors (ratio of subpopulations in the entire population) can have deleterious effect on the performance of any analysis on the dataset

Stratifications using IDEA software. What is IDEA !! Full form of IDEA is Interactive Data Extraction and Analysis. IDEA® Data Analysis Software is a comprehensive, powerful and easy-to-use data analysis solution designed by audit experts. With a modern, intuitive interface and advanced analytical functionalities, IDEA accelerates data analytics, provides a more user-friendly experience and enables deeper insights in a timely, cost-effective manner for more informed business decisions.. 

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What is IDEA !! Full form of IDEA is Interactive Data Extraction and Analysis.  IDEA® Data Analysis Software is a comprehensive, powerful and easy-to-use data analysis solution designed by audit experts. With a modern, intuitive interface and advanced analytical functionalities, IDEA accelerates data analytics, provides a more user-friendly experience and enables deeper insights in a timely, cost-effective manner for more informed business decisions.

STEP BY STEP PROCEDURE FOR PERFORMING STRATIFICTION. 

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STEP BY STEP PROCEDURE FOR PERFORMING STRATIFICTION

For example , We have Various types of Data of enterprise, We want to analyse data related to sales. We export the data into IDEA and Ensure that Sales Transaction-Sales Trans is the active database.

Continue . . .. IDEA - Sales Transaction-SalesTrans.IMD File Edit View Data Analysis Sampling Tools Window File Explorer u File Name ACCESS-Database Customer-Datab„. Sales Transaction... Sales-Sheetr Sample-Authoriz... Summarization... Pivot Table... Stratification... Duplicate Key Gap Detection... Aging... Benford's Law... Advanced Statistical Methods Help SalesTrans.1MD OATE INVOICENO -Apro 1001 1002 -Apr.æ ; ' 1034 •Apr•Ø -Apr& 15 7 8 9 01-Apr-Ø , 01-Apro , 01-Apr-09 1Ø7. 

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IDEA - Sales Transaction-SalesTrans.IMD
File Edit View Data Analysis Sampling
Tools
Window
File Explorer
u File
Name
ACCESS-Database
Customer-Datab„.
Sales Transaction...
Sales-Sheetr
Sample-Authoriz...
Summarization...
Pivot Table...
Stratification...
Duplicate Key
Gap Detection...
Aging...
Benford's Law...
Advanced Statistical Methods
Help
SalesTrans.1MD
OATE
INVOICENO
-Apro
1001
1002
-Apr.æ ;
' 1034
•Apr•Ø
-Apr&
15
7
8
9
01-Apr-Ø
, 01-Apro ,
01-Apr-09
1Ø7

IDEA  ↓ Analysis  ↓ Stratifications

Continue . . .. In the field To stratify box, select sales In the Fields to total box , select sales. Group by USERID Specify the stratifications Bands : Change the increment to certain number. Click the upper limit and text box of the first row. Note the text box is field with the certain number.. 

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In the field To stratify box, select sales In the Fields to total box , select sales. Group by USERID  Specify the stratifications Bands : Change the increment to certain number. Click the upper limit and text box of the first row. Note the text box is field with the certain number.

Continue . . .. ( After doing steps described in previous slide you will gate the window as below ). 

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( After doing steps described in  previous  slide you will gate the window as below )

OAT E
Ite.401CENO
usEHlO
V] SALE S
2
3

Now click on the second row of the spreadsheet, it will be filled with data within the described limits.

Continue . . .. Highlight the row of the spreadsheet area to take the range . Ensure the Create result check box is selected. In the Result name box, enter Stratification sales. Click OK The Numeric Stratification result output for the Sales Transactions database becomes active and appears as a link in the Results area of the Properties window.. 

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Highlight the row of the spreadsheet area to take the range . Ensure the Create result check box is selected. In the Result name box, enter Stratification sales. Click OK  The Numeric Stratification result output for the Sales Transactions database becomes active  and appears as a link in the Results area of the Properties window.

Continue . . .. Sk.ES SALES E 12 cm. Prepared By : Savan Modhavaniya. 

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You will gate the window (numeric stratifications ) as below.

Prepared By : Savan Modhavaniya. 28. THANK YOU. NSYOU. 