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Data Collection

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Data Collection Many economic, social and business questions need clarification and discussion. Solutions need to be presented and criteria agreed for their ... – PowerPoint PPT presentation

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Title: Data Collection


1
Data Collection
  • Many economic, social and business questions need
    clarification and discussion.
  • Solutions need to be presented and criteria
    agreed for their acceptance or rejection.
  • Decision makers not only need data but also need
    to evaluate the quality of data.

2
Data Collection contd.
  • Quantitative data can describe the size of a
    business, its profitability, its product range,
    and the characteristics of its workforce and a
    host of other factors.
  • However, numbers alone are unlikely to give us
    the understanding of the business problem that we
    require. We need to take account of the people
    involved, the culture of the enterprise, the
    legal and economic environment.

3
Data Collection contd.
  • Most significant business problems are likely to
    require a multi-disciplinary approach.
  • In fact, few problems are purely qualitative in
    nature.
  • Examples
  • If we consider the personnel problem of staff
    recruitment we soon begin to describe job
    requirements in terms of age, income and other
    measurable factors.

4
Data Collection contd.
  • If we consider another personnel problem of
    assessing training needs, then we can become
    involved in a major statistical exercise.

5
Problems of Data for Decision Maker
  • The completeness of data
  • The decision maker will need to decide whether
    the current data is sufficient for the purpose or
    whether additional data should be acquired. Data
    collection takes time and can be costly.
  • The quality of data
  • Data that have bias or are misleading can damage
    any effective decision making process.

6
Questions of data
  • A prerequisite of any statistical enquiry is an
    understanding of the purpose. The broad groups of
    question are
  • What is the relevant population?
  • What are the sources of data?
  • How many people were asked and how were they
    selected?
  • How was the information collected?
  • Who did not respond?
  • What type of data was selected?

7
Defining population
  • The term population can be used to describe all
    the items or organizations of interest.
  • For example, an audit is concerned with the
    correctness of financial statements. The
    population of interest to the auditor could be
    the accounting records, invoices or wage sheets.
  • In the case of job opportunities, the population
    could be all the local businesses or
    organizations employing one or more persons.

8
Identifying relevant population
  • Identification of the relevant population is
    essential since data collection can be a costly
    exercise and contacting large numbers of people
    who could have nothing to do with the survey will
    only waste these valuable resources.
  • If you are interested in why people bought
    foreign-built cars, but failed to contact
    purchasers of the imported models, then you might
    fail to identify the fact that some buyers do not
    realize that their car is foreign built.

9
Sample Frame
  • Having considered relevant population, the next
    problem is to identify who these people are and
  • To get a list of their names and addresses. If
    this list can be obtained, it is called a
    sampling frame.
  • Many surveys, particularly in market research
    need a general population of adults, and make use
    of the Electoral Register.

10
Sample Frame contd.
  • When a list does not exist or is not available,
    then those collecting the information may either
    try to compile a list, or use a method of
    collection which does not require a sampling
    frame.

11
Sample Size for Multivariate Analysis
  • Fairly large sample sizes are needed for
    multivariate analyses. The large sample size is
    necessary because the correlations used to
    calculate these statistics are not very stable
    when based on small samples.
  • Tabachnik and Fidell (2001, p. 117) offer the
    following formula for computing the sample size
    required for a multiple regression analysis
  • N 50 8m
  • Where m equals the number of predictor
    variables.
  • Tabachnick, B. G., Fidell, L. S. (2001) Using
    Multivariate Statistics (4th ed.). Boston Allyn
    and Bacon.

12
Sample Size for MA
  • So, if you have five predictor variables, you
    would need a minimum 90 participants in your
    sample.
  • Larger samples may be needed if your data are
    skewed, there is substantial measurement error,
    or you anticipate weak relationship among
    variables.
  • Care is also needed about too large a sample.
    With overly large samples, very weak
    relationships that may have neither theoretical
    nor practical value can achieve statistical
    significance.
  • Tabachnick, B. G., Fidell, L. S. (2001) Using
    Multivariate Statistics (4th ed.). Boston Allyn
    and Bacon.

13
Sample Size for MA
  • Several factors should be considered before using
    multivariate statistics.
  • Make sure that your data meet the assumptions of
    the test you are going to use (that is,
    normality, linearity, and homoscedasticity)
  • that you have removed any outlier or minimized
    their effects through transformation
  • that you have considered error of measurement
    and
  • that you have gathered a sufficiently large
    sample.
  • If you violate the assumptions of the test or
    fail to take into account the other important
    factors, the results you obtain may not be valid.

14
Critical steps
  • Considering the purpose of a statistical enquiry
    and defining the relevant population are the most
    important and critical steps in a market research
    survey.
  • If we are not sure about the purpose of the
    enquiry, and we are not selective about the
    information collected, what is the likely value
    of any subsequent, complex statistical analysis?
    This is surely similar to the computing saying
    GIGO garbage in, garbage out.

15
Data Collection
  • The next step is to obtain data on the population
    of interest.
  • A statistical enquiry may require the collection
    of new data, referred to as primary data, or be
    able to use existing data, referred to as
    secondary data, or may require some combination
    of both sources.

16
Data Collection Methods
  • Interviews
  • Face to face interviews, telephone interviews,
    computer-assisted interviews, and through the
    electronic media
  • Questionnaires
  • Personally administered, sent through the mail,
    or electronically administered and
  • observation of individuals and events with or
    without videotaping or audio recording.

17
Sources of data
  • Primary data sources
  • Individual, focus groups, and a panel of
    respondent specifically set up by the researcher
    whose opinions may be sought on specific issues
    from time to time

18
Sources of data contd.
  • Secondary data sources
  • Companys records or archives, government
    publications, industry analysis offered by media,
    web sites, the internet, and so on.
  • In some cases, the environment or particular
    settings and events may themselves be sources of
    data, as for example, studying the layout of a
    plant.

19
Sources of data contd.
  • Data can also be collected from case studies and
  • Any of the many sources of secondary data for
    analysis and application to solve specific
    problems.

20
Sources of data contd.
  • In survey research, the three main data
    collection methods are
  • Interviewing,
  • administering questionnaires, and
  • observing people and phenomena.

21
Sources of data contd.
  • Secondary data can come from within the
    organization, internal secondary data, or from
    outside the organization, external secondary
    data.

22
Internal external secondary data sources
  • Internal data sources include employee records,
    payroll information and customer orders.
  • External data are the official statistics
    supplied by the central statistical office and
    other government departments.

23
Exploring Secondary Data Exploratory Research
  • Expand understanding of management dilemma
  • Expand understanding of research question
  • Identify plausible investigative questions

24
Levels of Information
  • Primary sources
  • Secondary sources
  • Tertiary sources

25
Types of Information Sources
  • Indexes and Bibliographies
  • Dictionaries
  • Encyclopedias
  • Handbooks
  • Directories

26
Secondary Sources by Type
  • Indexes and Bibliographies
  • to find or locate books or articles
  • to find authors, topics to use in online searches
  • Dictionaries
  • to identify jargon of an industry--used for
    online searches
  • to identify bell-weather events in an industry
  • to identify knowledgeable people to interview
  • to identify organizations of influence
  • Encyclopedias
  • To identify historical or background information
  • To find critical dates within an industry
  • To find events of significance to the industry,
    company
  • Handbooks
  • To find facts relevant to topic
  • To identify influential individuals through
    source citations
  • Directories
  • To identify influential people and organizations
  • to find addresses, e-mail, other contact info on
    these people and organizations

27
Evaluating Information Sources
  • Purpose
  • Scope
  • Authority
  • Audience
  • Format

28
Evaluating Sources
  • Purpose
  • What the author is attempting to accomplish
  • identify hidden agenda(s)
  • identify direction of bias
  • Seek both biased and unbiased sources
  • Scope
  • Identify dates of inclusion and exclusion
  • Identify subjects of inclusion and exclusion
  • Authority
  • Identify background of author
  • Credentials educational, professional
  • Experience duration, setting, level
  • Identify the level of scholarship in content
  • footnotes, endnotes
  • Audience
  • Identify knowledge level and background
  • Identify orientation and bias
  • Seek biased and unbiased sources
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