Conducting Psychological Research

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Conducting Psychological Research

• Passer Chapter 2

• Slides Prepared by Alison L. OMalley

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What is good science? Jot down 3
characteristics
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Origins of Research Questions

• Personal experience and daily events
• Prior research and theory
• Real-world problems
• Serendipity

• Generate an example
• associated with each
• source.

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Conducting a Literature Search
Where to begin?
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Conducting a Literature Search

• Online databases PsycInfo, Google Scholar
• Boolean operators AND, NOT, OR to narrow results
  
• Peer-reviewed articles
• Full text access?
• If not, try authors personal websites
• or interlibrary loan (allow plenty of time!)

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Conducting a Literature Search
Research Question Are pet owners happier than
non pet owners? Whats the optimal Whats the
optimal Way way to enter this question
into a search database? In search
database?
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Making Sense of What You Find
Manuscript component Brief description
Abstract Short summary of study
Introduction Background and rationale for hypotheses
Method Participants, procedure, materials/measures
Results Data analysis statistical tests reveal support or lack thereof for hypotheses
Discussion Non-statistical review of findings, implications, limitations, avenues for future research
References List of all in-text citations formatted in APA style
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Making Sense of What You Find
Manuscript component Brief description
Abstract Short summary of study
Introduction Background and rationale for hypotheses
Method Participants, procedure, materials/measures
Results Data analysis statistical tests reveal support or lack thereof for hypotheses
Discussion Non-statistical review of findings, implications, limitations, avenues for future research
References List of all in-text citations formatted in APA style
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Making Sense of What You Find
Manuscript component Brief description
Abstract Short summary of study
Introduction Background and rationale for hypotheses
Method Participants, procedure, materials/measures
Results Data analysis statistical tests reveal support or lack thereof for hypotheses
Discussion Non-statistical review of findings, implications, limitations, avenues for future research
References List of all in-text citations formatted in APA style
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Making Sense of What You Find
Manuscript component Brief description
Abstract Short summary of study
Introduction Background and rationale for hypotheses
Method Participants, procedure, materials/measures
Results Data analysis statistical tests reveal support or lack thereof for hypotheses
Discussion Non-statistical review of findings, implications, limitations, avenues for future research
References List of all in-text citations formatted in APA style
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Making Sense of What You Find
Manuscript component Brief description
Abstract Short summary of study
Introduction Background and rationale for hypotheses
Method Participants, procedure, materials/measures
Results Data analysis statistical tests reveal support or lack thereof for hypotheses
Discussion Non-statistical review of findings, implications, limitations, avenues for future research
References List of all in-text citations formatted in APA style
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Making Sense of What You Find
Manuscript component Brief description
Abstract Short summary of study
Introduction Background and rationale for hypotheses
Method Participants, procedure, materials/measures
Results Data analysis statistical tests reveal support or lack thereof for hypotheses
Discussion Non-statistical review of findings, implications, limitations, avenues for future research
References List of all in-text citations formatted in APA style
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Making Sense of What You Find
Manuscript component Brief description
Abstract Short summary of study
Introduction Background and rationale for hypotheses
Method Participants, procedure, materials/measures
Results Data analysis statistical tests reveal support or lack thereof for hypotheses
Discussion Non-statistical review of findings, implications, limitations, avenues for future research
References List of all in-text citations formatted in APA style
Note. Review papers (e.g., Annual Review of
Psychology) will deviate from this format
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Forming a Hypothesis

• Inductive Specific facts ? general conclusion
• Data driven bottom up
• E.g., medical diagnosis based on symptoms
• Deductive General principle ? specific
  conclusion
• Theory driven top down
• E.g., All people have ___. Pat is a person.
  Therefore, Pat has ___.

• Is one logical approach better than the other?

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Research Approaches Key Distinctions

• Qualitative vs. Quantitative

• Describe the characteristics of a recent happy
  episode in your life.
• How happy are you?

1 2 3 4 5
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Research Approaches Key Distinctions

• Experimental vs. Descriptive

• Research Scenario 1 Employees randomly assigned
  to receive cookies or not receive cookies while
  completing a job satisfaction questionnaire
  (Brief, Butcher, Roberson, 1995)
• Research Scenario 2 Employees complete a
  questionnaire containing questions about mood and
  job satisfaction

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Research Approaches Key Distinctions

• Experimental vs. Descriptive

• Research Scenario 1 Employees randomly assigned
  to receive or not receive cookies while
  completing a job satisfaction questionnaire
  (Brief, Butcher, Roberson, 1995)
• Research Scenario 2 Employees complete a
  questionnaire containing questions about mood and
  job satisfaction

What can we conclude on the basis of each
research scenario? Why?
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Research Design Mind Your Variables

Independent variable Systematically manipulated
by the researcher in experimental
research Dependent variable Outcome of
interest what we design research to
assess/measure
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Research Design Mind Your Variables
Identify the IV(s) and DV(s) in this
scenario Employees randomly assigned to receive
cookies or not receive cookies while completing a
job satisfaction questionnaire

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Mastering IVs and DVs

• Generate and describe a good strategy for
  distinguishing independent variables from
  dependent variables in research scenarios.

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Research Approaches Key Distinctions

• Did employees complete the job satisfaction
  questionnaire under the same conditions (i.e., in
  identical environments), or did they take the
  questionnaire online at a time and place of their
  choosing?

• Laboratory vs. Field

Lab settings CONTROL
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Research Approaches Key Distinctions

• Laboratory vs. Field

• Field experiments still entail manipulation of an
  IV, but occur in a natural setting as opposed to
  a lab setting.
• Researchers often mention the tradeoff between
  internal and external validity. What exactly does
  this mean, and why does such a tradeoff occur?

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Research Approaches Key Distinctions

• Cross-sectional vs. Longitudinal

• 20 year olds
• 40 year olds
• 
  60 year olds

If all three age groups are measured and
compared in summer 2013, the design is
cross-sectional.
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Research Approaches Key Distinctions

• Cross-sectional vs. Longitudinal

• 20 year olds
• 40 year olds
• 
  60 year olds

If all three age groups are measured and compared
in summer 2013, the design is cross-sectional.
Beware of cohort effectsdifferent age groups
have different histories. Are observed difference
s due to age differences or the groups
different historical experiences?
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Research Approaches Key Distinctions

• Cross-sectional vs. Longitudinal

• 20 years old
  Summer 2013
• 40 years old
  Summer
  2033
• 
  60 years old
  Summer 2053

If a group of participants is measured repeatedly
over time, the design is longitudinal.
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Research Approaches Key Distinctions

• Cross-sectional vs. Longitudinal

• 20 years old
  Summer 2013
• 40 years old
  Summer
  2033
• 
  60 years old
  Summer 2053

If a group of participants is measured repeatedly
over time, the design is longitudinal.
Sequential research designs examine several age
cohorts longitudinally.
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Research Approaches Key Distinctions

• Cross-sectional vs. Longitudinal

• 20 years old
  Summer 2013
• 40 years old
  Summer
  2033
• 
  60 years old
  Summer 2053

What are the advantages and disadvantages of
longitudinal and sequential research designs?
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Research Design Mind Your Variables

• Internal validity is compromised by the presence
  of confounds, a particularly pesky type of
  extraneous variable.

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Research Design Mind Your Variables

• Example Do participants prefer stimuli
  associated with the first letter of the English
  alphabet?
• If random assignment is used such that half the
  participants see the object on the left and half
  see the object on the right, whats the problem?

A
B
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The Role of Sampling

• Population vs. Sample

• What is a population?
• The entire group of scores that a researcher
  desires to learn about (e.g., all U.S. college
  students)
• What is a sample?
• A subset of scores from the population (e.g.,
  1,000 college students from a variety of
  colleges)

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Analyzing Data and Drawing Conclusions

• Quantitative and qualitative analysis

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Descriptive Statistics

• Organize and summarize a set of data

• Measures of central tendency address the
  typicality of a score
• Mode most frequent score
• Median middle score (of an ordered set)
• Mean mathematical center of distribution

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Descriptive Statistics

• Central Tendency

• Build a dataset comprised of how many siblings
  each of your classmates has.
• Establish the mode, median, and mean for this
  dataset.

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Descriptive Statistics Central Tendency

• Is it more appropriate to report the mean or the
  median for men and women in this dataset? Why?

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Descriptive Statistics Measures of Dispersion

• Organize and summarize a set of data

• Measures of dispersion address the spread (i.e.,
  the variability) of a set of scores.

Sketch the distribution associated with each of
the three parties.
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Descriptive Statistics Measures of Dispersion

• Organize and summarize a set of data

• Measures of dispersion address the spread (i.e.,
  the variability) of a set of scores.

Range distance between highest and lowest score
Variance spread of scores relative to mean
Standard deviation square root of variance
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Inferential Statistics

• We use sample data to infer the nature of the
  population

• An oft heard question is whether research
  findings are statistically significant. Are our
  findings merely due to random errorto chance?
• Inferential statistics reveal the probability
  that our findings are due to chance.

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Inferential Statistics

• We use sample data to infer the nature of the
  population

• Psychological scientists traditionally maintain
  that findings are statistically significant if
  the probability is less than 5 that the results
  are due to random error.

p lt .05 ?
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Inferential Statistics Drawing Conclusions

• We use sample data to infer the nature of the
  population

• Statistically significant findings mean that
  weve proven how the world works, right?

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Inferential Statistics Drawing Conclusions

• We use sample data to infer the nature of the
  population

• Statistically significant findings mean that
  weve proven how the world works, right?
• WRONG.

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Inferential Statistics Drawing Conclusions

• We use sample data to infer the nature of the
  population

• Our results may not be practically important
• or perhaps there were confounding variables at
  play.
• Good research design is critical!
• And even with solid research design, maybe our
  conclusion is downright wrong.

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Drawing Conclusions

• Two errors False alarms and missed opportunities

An innocent person is found guilty ? False alarm
(Type I error) In research terms, we mistakenly
conclude that two variables are associated when
they really have nothing to do with each other.
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Drawing Conclusions

• Two errors False alarms and missed opportunities

A guilty person is found innocent ? Missed
opportunity (Type II error) In research terms,
we mistakenly conclude that two variables are not
associated when they really are related.
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Drawing Conclusions

• Two errors False alarms and missed opportunities

Apply the false alarm and missed opportunity
scenarios to the cookie experiment (Brief et
al., 1995).
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How to Tell Your Research Story

• So we all speak the same language!

• Run, dont walk, to access the 6th edition of the
  APA publication manual!
• http//www.apastyle.org/

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Building Knowledge and Theories

• Contemplate the distinction between a theory and
  a hypothesis
• Now, why does theory building matter?

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What Makes a Good Theory?

• Testability and specificity
• Does theory lend itself to testable
  hypotheses and specific predictions?
• Internal consistency and clarity
• Does theory avoid contradictory
  predictions? Can it be falsified? Is it clear
  to experts how components of the theory relate
  to each other?

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What Makes a Good Theory?

• Empirical support
• Can theory be reconciled with current knowledge
  base? If not, can it debunk current fact? Does
  high quality research support new hypotheses
  derived from theory?
• Parsimony
• Law of parsimony Explanations should use the
  minimum number of principles necessary to
  account for the maximum number of facts.

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What Makes a Good Theory?

Last, but not least Does the theory have an
impact on the field?
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Proof and Disproof

• Research is more probabilistic than absolute
  (Baumeister, 2008)

• Science values lively debate. There is no
  tolerance for the notion of absolute proof.
  Its always possible that our results are due to
  chance. Similarly, a single set of results cannot
  disprove a hypothesis derived from a theory.
• Science is forward-moving, and theories are
  strengthened or weakened as supportive or
  unsupportive findings continually emerge.