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Assumption University of Thailand

2020

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information gathering sampling methods systems analysis data analysis

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Systems Analysis & Design Tenth Edition Global Edition Chapter 5 Information Gathering: Unobtrusive Methods Copyright © 2020 Pearson Education Ltd. Learning Objectives (1 of 2) 5.1 Recogn...

Systems Analysis & Design Tenth Edition Global Edition Chapter 5 Information Gathering: Unobtrusive Methods Copyright © 2020 Pearson Education Ltd. Learning Objectives (1 of 2) 5.1 Recognize the value of unobtrusive methods for information gathering 5.2 Understand the concept of sampling for human information requirements analysis 5.3 Construct useful samples of people, documents, and events for determining human information requirements Copyright © 2020 Pearson Education Ltd. Learning Objectives (2 of 2) 5.4 Interpret managers’ and customers’ messages, interviews, and communications using text analytics 5.5 Create an analyst’s playscript to observe decision-maker activities 5.6 Apply the STROBE technique to observe and interpret the decision maker’s environment Copyright © 2020 Pearson Education Ltd. Unobtrusive Methods Less disruptive Text analytics to analyze qualitative data Insufficient when used alone Multiple methods approach Used in conjunction with interactive methods Copyright © 2020 Pearson Education Ltd. Major Topics Sampling Quantitative document analysis Qualitative document analysis Text analytics Observation STR OBE Applying STROBE Copyright © 2020 Pearson Education Ltd. Sampling (1 of 2) A process of systematically selecting representative elements of a population Involves two key decisions: – What to examine – Which people to consider Copyright © 2020 Pearson Education Ltd. Need for Sampling (1 of 2) The reasons systems analysts do sampling are to – Contain costs – Speed up data gathering – Improve effectiveness – Data gathering bias can be reduced by sampling Copyright © 2020 Pearson Education Ltd. Need for Sampling (2 of 2) Too costly to – Examine every scrap of paper – Talk with everyone – Read every Web page from the organization Copyright © 2020 Pearson Education Ltd. Sampling (2 of 2) Sampling helps accelerate the process by gathering selected data rather than all data for the entire population The systems analyst is spared the burden of analyzing data from the entire population Copyright © 2020 Pearson Education Ltd. Sampling Effectiveness Sampling can help improve effectiveness if information that is more accurate can be obtained This is accomplished by talking to fewer employees but asking them questions that are more detailed If fewer people are interviewed, the systems analyst has more time to follow up on missing or incomplete data Copyright © 2020 Pearson Education Ltd. Sampling Bias Data gathering bias can be reduced by sampling When the systems analyst asks for an opinion about a permanent feature of the installed information system, the executive interviewed may provide a biased evaluation because there is little possibility of changing it Copyright © 2020 Pearson Education Ltd. Sampling Design To design a good sample, a systems analyst must 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 Copyright © 2020 Pearson Education Ltd. Figure 5.1 Four Main Types of Samples the Analyst Has Available Copyright © 2020 Pearson Education Ltd. Four Main Types of Samples Convenience Purposive Simple random Complex random Copyright © 2020 Pearson Education Ltd. Convenience Samples Convenience samples are unrestricted, nonprobability samples This sample is the easiest to arrange The most unreliable Copyright © 2020 Pearson Education Ltd. Purposive Sample A purposive sample is based on judgment Choose a group of individuals who appear knowledgeable and are interested in the new information system A nonprobability sample Only moderately reliable Copyright © 2020 Pearson Education Ltd. Complex Random Samples The complex random samples that are most appropriate for a systems analyst are – Systematic sampling – Stratified sampling – Cluster sampling Copyright © 2020 Pearson Education Ltd. The Sample Size Decision Determine the attribute Locate the database or reports in which the attribute can be found Examine the attribute Make the subjective decision regarding the acceptable interval estimate Choose the confidence level Calculate the standard error Determine the sample size Copyright © 2020 Pearson Education Ltd. Figure 5.2 A Table of Area under a Normal Curve Can Be Used to Look up a Value Once the Systems Analyst Decides on the Confidence Level Copyright © 2020 Pearson Education Ltd. Calculate the Standard Error of the Proportion i p  Z i = interval estimate z = confidence coefficient found in the confidence level lookup table Copyright © 2020 Pearson Education Ltd. Determine the Sample Size p  1 p  n 2 1    = standard error  = the proportion of the population having the attribute Copyright © 2020 Pearson Education Ltd. Example: A. Sembly Company Determine that you are looking for orders with mistakes Locate order forms from the past six months Examine order forms and conclude that p = 5% i  0.02 Subjective decision of acceptable interval Look up confidence coefficient z - value = 1.96 i 0.02 Calculate  p    0.0102 z 1.96 Determine n; n = 458 Copyright © 2020 Pearson Education Ltd. Investigation The act of discovery and analysis of data Hard data – Quantitative – Qualitative Copyright © 2020 Pearson Education Ltd. Analyzing Quantitative Documents Reports used for decision making Performance reports Records Data capture forms Ecommerce and other transactions Copyright © 2020 Pearson Education Ltd. Reports Used for Decision Making Sales reports Production reports Summary reports Copyright © 2020 Pearson Education Ltd. Figure 5.3 A Performance Report Showing Improvement Copyright © 2020 Pearson Education Ltd. Figure 5.4 A Manually Completed Payment Record Copyright © 2020 Pearson Education Ltd. Records Records provide periodic updates of what is occurring in the business There are several ways to inspect a record: – Checking for errors in amounts and totals – Looking for opportunities for improving the recording form design – Observing the number and type of transactions – Watching for instances in which the computer can simplify the work (calculations and other data manipulation) Copyright © 2020 Pearson Education Ltd. Data Capture Forms Collect examples of all the forms in use Note the type of form Document the intended distribution pattern Compare the intended distribution pattern with who actually receives the form Copyright © 2020 Pearson Education Ltd. Figure 5.5 Questions to Ask about Official and Bootleg Forms That Are Already Filled out Copyright © 2020 Pearson Education Ltd. Questions to Ask about Forms Is the form filled out in its entirety? Are there forms that are never used? Are all copies of forms circulated to the proper people or filed appropriately? Check on permissions and functioning form links. Can people who must access online forms do so? If there is a paper form that is offered as an alternative to a Web-based form, compare the completion rates for both. Are “unofficial” forms being used on a regular basis? Copyright © 2020 Pearson Education Ltd. Analyzing Qualitative Documents (1 of 2) 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 Copyright © 2020 Pearson Education Ltd. Analyzing Qualitative Documents (2 of 2) Email messages Memos Signs or posters on bulletin boards Corporate Web sites (note the interactivity of Web sites) Manuals Policy handbooks Copyright © 2020 Pearson Education Ltd. Figure 5.6 Analysis of Memos Provides Insight into the Metaphors That Guide the Organization’s Thinking Copyright © 2020 Pearson Education Ltd. Text Analytics (1 of 3) Software that can analyze unstructured qualitative data from any source including: – Transcripts of interviews – Written reports – Customers’ communication collected through email, wikis, blogs, chat rooms, and other social networking sites Copyright © 2020 Pearson Education Ltd. Text Analytics (2 of 3) Unstructured, qualitative, or “soft” data are generated through: – Blogs – Chat rooms – Questionnaires using open-ended questions – Online discussions conducted on the Web – Exchanges occurring on social media Copyright © 2020 Pearson Education Ltd. Text Analytics Can Help Text analytics can realize valuable insights into – What customers are thinking about the organization, the values and actions of the company – Customer or vendor motivations for beginning, maintaining, improving, or discontinuing a relationship Copyright © 2020 Pearson Education Ltd. Figure 5.7 Concept Map Showing Prominence and Relationships of Concepts Copyright © 2020 Pearson Education Ltd. Figure 5.8 Ranked Concepts for Categories Overview Copyright © 2020 Pearson Education Ltd. Text Analytics (3 of 3) Text analytics provide insights for an organization’s members who want to have a rapid and visual yet decidedly qualitative approach to analyzing text data An important element is to design the human activities surrounding the use of text analytics software Copyright © 2020 Pearson Education Ltd. Observation Observation provides insight on what organizational members actually do See firsthand the relationships that exist between decision makers and other organizational members Can also reveal important clues regarding HCI concerns Copyright © 2020 Pearson Education Ltd. Analyst’s Playscript Involves observing the decision-makers behavior and recording their actions using a series of action verbs Examples: – Talking – Sampling – Corresponding – Deciding Copyright © 2020 Pearson Education Ltd. Figure 5.9 A Sample Page from the Analyst’s Playscript Copyright © 2020 Pearson Education Ltd. STROBE (1 of 2) STRuctured OBservation of the Environment—a technique for observing the decision-maker’s physical environment Copyright © 2020 Pearson Education Ltd. STROBE (2 of 2) Often it is possible to observe the particulars of the surroundings that will confirm or negate the organizational narrative – Also called stories or dialogue – Information that is found through interviews or questionnaires Copyright © 2020 Pearson Education Ltd. STROBE Elements Office location Desk placement Stationary equipment Props External information sources Office lighting and color Clothing worn by decision makers Copyright © 2020 Pearson Education Ltd. Figure 5.10 Seven Concrete Observable Elements of STROBE Observable Element Questions an Analyst Might Investigate Office location Who has the corner office? Are the key decision makers dispersed over separate floors? Desk placement Does the placement of the desk encourage communication? Does the placement demonstrate power? Stationary equipment Does the decision maker prefer to gather and store information personally? Is the storage area large or small? Props Is there evidence that the decision maker uses a P C, smartphone, or tablet computer in the office External information Does the decision maker get much information from external sources sources such as trade journals or the Web? Office lighting and color Is the lighting set up to do detailed work or more appropriate for casual communication? Are the colors warm and inviting? Clothing worn by decision Does the decision maker show authority by wearing conservative suits? makers Are employees required to wear uniforms? Copyright © 2020 Pearson Education Ltd. Office Location Who has the corner office? Are the key decision makers dispersed over separate floors? Copyright © 2020 Pearson Education Ltd. Desk Placement Does the placement of the desk encourage communication? Does the placement demonstrate power? Copyright © 2020 Pearson Education Ltd. Stationary Office Equipment Does the decision maker prefer to gather and store information personally? Is the storage area large or small? Copyright © 2020 Pearson Education Ltd. Props Is there evidence that the decision maker uses a P C, smart phone, or tablet computer in the office? Copyright © 2020 Pearson Education Ltd. External Information Sources Does the decision maker get much information from external sources such as trade journals or the Web? Copyright © 2020 Pearson Education Ltd. Office Lighting and Color Is the lighting set up to do detailed work or more appropriate for casual communication? Are the colors warm and inviting? Copyright © 2020 Pearson Education Ltd. Clothing Does the decision maker show authority by wearing conservative suits? Are employees required to wear uniforms? Copyright © 2020 Pearson Education Ltd. Figure 5.11 STROBE and Decision-Maker Characteristics Characteristics of Decision Makers Corresponding Elements Characteristics of Decision Makers in the Physical Environment Gathers information informally Warm, incandescent lighting and colors Seeks extraorganization information Trade journals present in office Processes data personally PCs, or tablet computers present in office Stores information personally Equipment/files present in office Exercises power in decision making Desk placed for power Exhibits credibility in decision making Wears authoritative clothing Shares information with others Office easily accessible Copyright © 2020 Pearson Education Ltd. Applying STROBE The five symbols used to evaluate how observation of the elements of STROBE compared with interview results are: – A checkmark means the narrative is confirmed – An “X” means the narrative is reversed – An oval or eye-shaped symbol serves as a cue to look further – A square means observation modifies the narrative – A circle means narrative is supplemented by observation Copyright © 2020 Pearson Education Ltd. Figure 5.12 An Anecdotal List with Symbols Copyright © 2020 Pearson Education Ltd. Summary (1 of 2) Sampling – Designing a good sample – Types of samples – Sample size Hard data – Quantitative document analysis – Qualitative document analysis Copyright © 2020 Pearson Education Ltd. Summary (2 of 2) Observation Text analytics STR OBE – STROBE elements – Applying STROBE Copyright © 2020 Pearson Education Ltd.

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