Chapter Nine

1
Chapter Nine

• Understanding Bias

2
The Nature of Bias

• Research bias may be thought of as a preference
  or predisposition to favour a particular outcome
  thus indicating a systematic distortion of
  research conclusions
• Typically the distortions are inadvertent, but
  they can also be intentional
• If bias is not addressed ina study, the
  reliability of findings is considered suspect

3
Bias in Quantitative Research

• Bias implies there is an unknown truth waiting to
  be described
• In quantitative designs the approach suggests
  that the researcher knows the truth simply
  wants to confirm his/her knowledge
• eg. Questionnaire responses reflect the
  researchers previous knowledge, values, etc.

4
Bias in Qualitative Research

• In qualitative research the researcher is an
  integral part of the research design the
  participants world
• To minimize bias in qualitative designs the
  researcher incorporates it into the design by
• bracketing
• audit trail
• selecting unfamiliar participants
• selecting a topic that is not too close to the
  researcher on a personal level

5
Triple Biases Nursing, Science, Culture...

• We take our nursing predispositions with us to
  our research projects
• We tend to seek corroboration of our
  preconceptions, helps to make sense of a
  complicated world, reaffirms our pet theories
• Science itself potentially blinds researchers
  because of the expectations of findings, the
  belief in certain theories. Like culture, science
  produces blinders

6
Sexism A Form of Bias

• Sexism is discrimination on the basis of gender
• RCTs used mainly by medicine have made women
  victims of this approach, studying women mainly
  as objects, ignoring their needs experiences

7
Types of Sexism in Research

• Androcentricity (male perspective)-based on
  individualism, materialism, competitiveness -
  in contrast to the views of women, ethnic groups,
  the poor who focus on family concerns rather
  than themselves
• Overgeneralization overspecificity
• Gender Insensitivity
• Familism - treating family as unit of analysis,
  rather than the individual

8
Sources of Bias...

• need to design studies to systematically test
  alternative explanations
• researcher affect refers to the bias that results
  from a researcher having fallen in love with some
  pet theory or explanation

9
Bias Selection of Problem

• Some things judged more important by funding
  agencies, ones discipline peers
• bias is toward the conventional, standard
  projects the selection of variables
  conventionally considered important the
  exclusion of those conventionally considered
  unimportant
• probably still a bias toward quantitative
  approaches

10
Bias Sampling Design

• results may be distorted by choosing to study
  sub-populations with known slants
• attitudes toward abortion in an urban community
  with a free standing abortion clinic vs rural
  communities
• bias is problematic in studies where the sample
  self-selects to participate

11
Bias Funding

• SSHRC main funding for social science research
• special funding available in hot areas
• traditional areas better funding
• NSERC and CIHR are better funded
• research in a social context

12
Bias Data Collection

• Experimenter effect reference is to the
  influence of experimenter preferences and
  expectations
• Robert Rosenthal the smart rats study.
• Clever Hans
• Expectancy
• Demand characteristics

13
Bias Data Analysis

• Coding Errors
• Random Error
• Systematic Error
• Data Massaging
• Hunting

14
Bias Reporting of Findings

• T.D. Sterling, 1959, 1995. Notes the problem of
  journals publishing only statistically
  significant findings.

15
Bias Funding

• Possibility of funding agencies to determine what
  is important to know have they got it right?
• Problems of emerging disciplines in competing
  with established ones

16
Advocacy Versus Pure Research

• P. 313.
• Mainstream research supportive of established
  interests in society. Is there a legitimate
  place to support the interests of minorities,
  women, people with disabilities, the working
  poor, the homeless

17
Rules for Minimizing Bias

• Education
• Avoid Sexism
• Advocacy or Explanation?
• Descriptive Accuracy
• Let disconfirmation be your guide
• Policy Recommendations are Value Based
• Be skeptical of Research Findings

18
Rules Cont

• Read Literature Cautiously, Skeptically
• Distinguish Advocacy from Pure Research
• Use Theory to Generate Testable Hypotheses
• Be Sensitive to your own outcome preferences
• Do not disclose hypotheses to subjects or
  assistants

19
Rules Cont.

• Be Accepting of All Responses
• Specify Data Analysis Procedures in Advance
• Check for Random Systematic Errors
• Report any Data Massaging