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Secondary Data, Literature Reviews, and Hypotheses

Secondary Data, Literature Reviews, and Hypotheses. 3. Learning Objectives. Understand the nature and role of secondary data Describe how to conduct a literature review Identify sources of internal and external secondary data Discuss conceptualization and its role in model development

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Secondary Data, Literature Reviews, and Hypotheses

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  1. Secondary Data, Literature Reviews, and Hypotheses 3

  2. Learning Objectives • Understand the nature and role of secondary data • Describe how to conduct a literature review • Identify sources of internal and external secondary data • Discuss conceptualization and its role in model development • Understand hypotheses and independent and dependent variables

  3. Cisco’s Connect Online

  4. NPDs Retail Tracking

  5. Nature and Scope of Secondary Data Internal External

  6. What is a Literature Review? • A literature review is a comprehensive examination of available information that is related to your research topic

  7. Reasons for Conducting a Literature Review • Clarify the research problem and questions • Uncover existing studies • Suggest research hypotheses • Identify scales to measure variables and methods

  8. Assessing Quality of Secondary Data Purpose Credibility Accuracy Methodology Consistency Bias

  9. Descriptive Variables Sought in Secondary Data Research • Demographic dimensions • Employment characteristics • Economic data • Competitive characteristics • Supply characteristics • Regulations • International market characteristics

  10. Sales invoices Accounts receivable reports Quarterly sales reports Sales activity reports Online registration Customer letters/ comments Mail-order forms Credit applications Warranty cards Past studies Sales person expense forms Sources of Internal Secondary Data

  11. Primary Sources of External Data Popular Sources Scholarly Sources Government Sources NAICS Guidebooks Commercial Sources

  12. Google Scholar

  13. Lexus Nexus

  14. Secondary Data, U.S. Government • U.S. Census Data • U.S. Census Reports • U.S. Department of Commerce Data • Additional Reports US Census http://www.census.gov/ BLS http://www.bls.gov/

  15. Syndicated Sources • Commercial vendors collect information and sell the reports • 80%+ of firms said they purchase and use reports and spend 10 hours per week analyzing this information • Institutional Syndicated Data • Consumer Syndicated Data

  16. Benefits Lower cost than other methods Rapid availability and timeliness Accurate reporting of sensitive purchases High level of specificity Risks Sampling error (low minority representation) Turnover of panel members Response bias (SDR) Consumer Panels

  17. NPD Group

  18. Sample Media Panel Data Sources

  19. Store Audits • Examination of how much of a particular product or brand has sold at retail level • Product sales in relation to competition • Effectiveness of shelf space/POP displays • Sales at various price points • Effectiveness of POS coupons • Direct sales by store type, location, etc

  20. Components of a Conceptual Model • A variable is an observable item that is used as a measured on a questionnaire • A construct is an unobservable concept that is measured by a group of related variables • Relationships are associations between two or more variables • Independent variables are variables or constructs that predict or explain the outcome of interest • Dependent variables are variables or constructs that researchers seek to explain

  21. Conceptualization • Conceptualization refers to the development of a model that shows variables and hypothesized or proposed relationships between variables

  22. Process of Conceptualization • Identify variables for research • Specify hypotheses and relationships • Prepare a diagram that represents the relationships visually

  23. Relationships Among Variables • Hypotheses can suggest negative or positive relationships • An association between two variables in which they increase or decrease together suggests a positive relationship • An association between two variables in which one increases while the other decreases describes a negative relationship

  24. Exhibit 3.8 A Model of New Technology Adoption

  25. Hypotheses • A hypothesis is an empirically testable though yet unproven statement developed in order to explain phenomena • Types of hypotheses include • Null or Alternate • Nondirectional • Inverse (negative) directional • Direct (positive) directional

  26. Parameters and Sample Statistics • A parameter is the true value of a variable, while a sample statistic is the value of a variable based on estimates from a sample

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