1 / 30

QUANTITATIVE DESIGN AND ANALYSIS MARK 2048

QUANTITATIVE DESIGN AND ANALYSIS MARK 2048. Instructor: Armand Gervais Email:. Learning Objectives. 1. Illustrate the process of preparing data for preliminary analysis. 2. Demonstrate the procedure for assuring data validation.

lulu
Télécharger la présentation

QUANTITATIVE DESIGN AND ANALYSIS MARK 2048

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. QUANTITATIVE DESIGN AND ANALYSIS MARK 2048 Instructor: Armand Gervais Email:

  2. Learning Objectives 1.Illustrate the process of preparing data for preliminary analysis. 2. Demonstrate the procedure for assuring data validation. 3. Illustrate the process of editing and coding data obtained through survey methods. 4. Acquaint the user with data entry procedures. 5. Illustrate a process for detecting errors in data entry. 6. Discuss techniques used for data tabulation and data analysis.

  3. Illustrate the process of preparing data for preliminary analysis Exhibit 14.1

  4. Value of Preparing Data for Analysis Illustrate the process of preparing data for preliminary analysis • Data Preparation– • process of converting information from a questionnaire so it can be transferred to a data warehouse • 4-step Approach • Data validation • Editing and coding of the data • Data entry • Data tabulation • Error Detection • Purpose of Data Preparation and Analysis

  5. Demonstrate the procedure for assuring data validation Data Validation • Data Validation– • to determine if the survey’s interviews or observations were conducted correctly and free of interviewer fraud or bias • Curbstoning

  6. Demonstrate the procedure for assuring data validation Data Validation • Process of Validation • Fraud • Screening • Procedure • Completeness • Courtesy

  7. Data Editing and Coding Illustrate the process of editing and coding data obtained through survey methods • Editing • process where by the raw data are checked for mistakes made by either the interviewer or the respondent • Areas of Concern • Asking the Proper Questions • Accurate Recording of Answers • Correct Screening Questions • Responses to Open-Ended Questions

  8. Illustrate the process of editing and coding data obtained through survey methods Exhibit 14.3

  9. The Coding Process Illustrate the process of editing and coding data obtained through survey methods • Coding • grouping and assigning values to various responses from the survey instrument • Codes are numerical–a number from 0-9 • Well planned and constructed questionnaires reduce time spent on coding • Numeric codes should be designed into the questionnaire from the beginning • Questionnaires that do not use coded responses–a master code must be established

  10. Illustrate the process of editing and coding data obtained through survey methods Exhibit 14.4

  11. Data Editing and Coding Illustrate the process of editing and coding data obtained through survey methods • Open-end questions–four-step process • Generate a master list of potential responses–assign values to the responses • Consolidate responses • Assign a numerical value as a code • Assign a coded value to each response

  12. Illustrate the process of editing and coding data obtained through survey methods Exhibit 14.5

  13. Acquaint the user with data entry procedures Data Entry • Data entry • Four major ways for entering coded data • Most popular option–personal computer (PC) • Touch screen • Light pen • Scanners • Primary purpose of data entry

  14. Acquaint the user with data entry procedures Exhibit 14.6

  15. Error Detection Illustrate a process for detecting errors in data entry • First step in Error Detection– • to determine if the software used for data entry and tabulation–allows error edit routines • To identify the wrong type of data • To review a printed representation of the entered data • To produce a data/column list procedure for the entered data • To find the appropriate questionnaire and verify the proper response (code)

  16. Exhibit 14.7 Illustrate a process for detecting errors in data entry

  17. Exhibit 14.8 Illustrate a process for detecting errors in data entry

  18. Discuss techniques used for data tabulation and data analysis Data Tabulation • Tabulation • Simple process of counting the number of observations that are classified into certain categories • Simple one-way tabulation • Cross-tabulation • Use and purpose of tabulation • Range from further validation of the accuracy of the data to the reporting of research results

  19. Discuss techniques used for data tabulation and data analysis Data Tabulation • One-way Tabulations 1. To determine the degree of non-response to individual questions • To locate blunders of simple errors in the data entry • To calculate summary statistics on various questions • Means • Standard deviations • Related descriptive statistics • To communicate the results of the research project

  20. Discuss techniques used for data tabulation and data analysis Data Tabulation • One-way frequency table– • the number of respondents who responded to each possible response to a questions given the available alternatives • One-way Frequency Table Identify • Missing data • Determining valid percentages • Summary statistics

  21. Exhibit 14.9 Illustrate a process for detecting errors in data entry

  22. Exhibit 14.10 Illustrate a process for detecting errors in data entry

  23. Discuss techniques used for data tabulation and data analysis Data Tabulation • Cross-tabulations • to determine whether certain variables differ when compared among various subgroups of the total sample • Primary form of data analysis • Key elements • How to develop the cross-tabulation • How to interpret the outcome • Shows frequencies and percentages, with percentages existing for both rows and columns

  24. Exhibit 14.11 Illustrate a process for detecting errors in data entry

  25. Discuss techniques used for data tabulation and data analysis Data Tabulation • Issues to Be Considered • The judgment of the analyst–the selection of variable (question) to use when examining relationships • Demographic variables or lifestyle/psychographic characteristics–the starting point in developing cross-tabulations • Technique is simple–often difficult to interpret • Keep research objectives in mind when constructing and using tables • Spreadsheets help

  26. Discuss techniques used for data tabulation and data analysis Data Tabulation • Descriptive Statistics • to summarize and describe the data obtained from a sample of respondents • Two Types of Measures • Central tendency • Dispersion

  27. Exhibit 14.12 Illustrate a process for detecting errors in data entry

  28. Discuss techniques used for data tabulation and data analysis Data Tabulation • Graphical Illustration • translation of one-way frequency and cross-tabulation tables into graphs • Graphical illustrations • a very powerful technique for communicating key research results generated from preliminary data analysis to the client

  29. Summary • Value of Preparing Data for Analysis • Data Validation • Data Editing and Coding • The Coding Process • Data Entry • Error Detection • Data Tabulation

  30. To Do’s • Compete LAB 1 for beginning of next class 5% • Complete assigned readings • Read Research Document

More Related