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Chapter 4 Sampling and Investigating Hard Data

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1 Chapter 4 Sampling and Investigating Hard Data
Systems Analysis and Design Kendall and Kendall Fifth Edition

2 Copyright © 2002 by Prentice Hall, Inc.
Major Topics Sampling Hard data Qualitative document analysis Workflow analysis Business process reengineering Archival documents Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

3 Copyright © 2002 by Prentice Hall, Inc.
Sampling Sampling is a process of systematically selecting representative elements of a population Involves two key decisions Which of the key documents and Web sites should be sampled Which people should be interviewed or sent questionnaires Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

4 Copyright © 2002 by Prentice Hall, Inc.
Need for Sampling The reasons systems analysts do sampling are Reduction of costs Speeding up the data-gathering process Improving effectiveness Reduction of data-gathering bias Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

5 Copyright © 2002 by Prentice Hall, Inc.
Sampling Design Steps To design a good sample, a systems analyst needs to follow four steps: Determining the data to be collected or described Determining the population to be sampled Choosing the type of sample Deciding on the sample size Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

6 Copyright © 2002 by Prentice Hall, Inc.
Sample Size The sample size decision should be made according to the specific conditions under which a systems analysts works with such as Sampling data on attributes Sampling data on variables Sampling qualitative data Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

7 Copyright © 2002 by Prentice Hall, Inc.
Types of Sampling There are four types of sampling Convenience Purposive Simple random Complex random Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

8 Copyright © 2002 by Prentice Hall, Inc.
Convenience Sampling Convenience samples are unrestricted, nonprobability samples Easy to arrange Most unreliable Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

9 Copyright © 2002 by Prentice Hall, Inc.
Purposive Sampling Based on judgment Analyst chooses group of individuals to sample Based on criteria Nonprobability sample Moderately reliable Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

10 Simple Random Sampling
Based on a numbered list of the population Each person or document has an equal chance of being selected Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

11 Complex Random Sampling
Has three forms Systematic sampling Stratified sampling Cluster sampling Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

12 Copyright © 2002 by Prentice Hall, Inc.
Systematic Sampling Simplest method of probability sampling Choose every kth person on a list Not good if the list is ordered Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

13 Copyright © 2002 by Prentice Hall, Inc.
Stratified Sampling Identifying subpopulations or strata Selecting objects or people for sampling from the subpopulation Compensates for a disproportionate number of employees from a certain group Most important to the systems analyst Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

14 Copyright © 2002 by Prentice Hall, Inc.
Cluster Sampling Select group of documents or people to study Select typical groups that represent the remaining ones Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

15 Deciding Sample Size for Attribute Data
Steps to determine sample size Determine the attribute to sample Locate the database or reports where the attribute is found Examine the attribute and estimate p, the proportion of the population having the attribute Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

16 Deciding Sample Size for Attribute Data
Steps to determine sample size (continued) Make the subjective decision regarding the acceptable interval estimate, i Choose the confidence level and look up the confidence coefficient (z value) in a table Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

17 Deciding Sample Size for Attribute Data
Steps to determine sample size (continued) Calculate σp, the standard error of the proportion as follows: i σp = z Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

18 Deciding Sample Size for Attribute Data
Steps to determine sample size (continued) Determine the necessary sample size, n, using the following formula: p(1-p) n = σp2 Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

19 Confidence Level Table
Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

20 Sample Size for Data on Variables
The steps to determine the sample size when sampling data on variables are Determine the variable you will be sampling Locate the database or reports where the variable can be found Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

21 Sample Size for Data on Variables
The steps to determine variable sample size (continued) Examine the variable to gain some idea about its magnitude and dispersion It would be useful to know the mean to determine a more appropriate interval estimate and the standard deviation, s to determine sample size (in the last step) Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

22 Sample Size for Data on Variables
The steps to determine variable sample size (continued) Make a subjective decision regarding the acceptable interval estimate, i Choose a confidence level and look up the confidence coefficient (z value) Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

23 Sample Size for Data on Variables
The steps to determine variable sample size (continued) Calculate σx, the standard error of the mean as follows: i σx = z Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

24 Sample Size for Data on Variables
The steps to determine variable sample size (continued) Determine the necessary sample size, n, using the following formula: 2 s n = σx2 Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

25 Copyright © 2002 by Prentice Hall, Inc.
Hard Data In addition to sampling, investigation of hard data is another effective method for systems analysts to gather information Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

26 Copyright © 2002 by Prentice Hall, Inc.
Obtaining Hard Data Hard data can be obtained by Analyzing quantitative documents such as records used for decision making Performance reports Records Data capture forms Ecommerce and other transactions Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

27 Qualitative Documents
Examine qualitative documents for the following: Key or guiding metaphors Insiders vs. outsiders mentality What is considered good vs. evil Graphics, logos, and icons in common areas or Web pages A sense of humor Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

28 Analyzing Qualitative Documents
Qualitative documents include Memos Signs on bulletin boards Corporate Web sites Manuals Policy handbooks Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

29 Copyright © 2002 by Prentice Hall, Inc.
Workflow Analysis Workflow analysis may reveal signs of larger problems, such as Data or information doesn’t flow as intended Bottlenecks in the processing of forms Access to online forms is cumbersome Unnecessary duplication of work occurs because employees are unaware that information is already in existence Employees lack understanding about the interrelatedness of information flow Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

30 Business Process Reengineering
Business process reengineering software includes the following features: Modeling of the existing system Analysis of possible outcomes Simulation of proposed work flow Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

31 Copyright © 2002 by Prentice Hall, Inc.
Archival Documents A systems analyst may obtain some valuable information by abstracting data from archival documents Generally, archival documents are historical data, and they are prepared and kept by someone else for specific purposes Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

32 Guidelines for Abstracting Archival Data
Fragment data into subclasses and make cross-checks to reduce errors Compare reports on the same phenomenon by different analysts Realize the inherent bias associated with original decisions to file, keep, or destroy reports Use other methods to obtain data Kendall & Kendall Copyright © 2002 by Prentice Hall, Inc.

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