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PS 0700 RESEARCH METHODS IN POLITICAL SCIENCE

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Title: PS 0700 RESEARCH METHODS IN POLITICAL SCIENCE


1
PS 0700 RESEARCH METHODS IN POLITICAL SCIENCE
  • UNIT 2 FUNDAMENTALS OF EMPIRICAL INQUIRY

2
1. SPECIFYING THE RESEARCH QUESTION
  • WHY? Why are Some Bureaucratic Agencies More
    Responsive to Political Influence than Others?
  • HOW? How does Information Flow Through the Mass
    Public?
  • WHEN? Does the Timing of Position Taking on
    Legislation Affect Material Benefits Obtained by
    MCs

3
1. SPECIFYING THE RESEARCH QUESTION
  • Common Features
  • Grounded in Empirical Observation(s)
  • Falsification is a Must
  • Aim is to Make Empirical Generalizations

4
1. SPECIFYING THE RESEARCH QUESTION
  • The Research Question Must Be Compelling That
    is, find an Object (i.e., Phenomenon) of Broad
    Interest that you seek to Better Understand.
    Explanadum/Dependent Variable

5
1. SPECIFYING THE RESEARCH QUESTION
  • Arrive at a Plausible Explanation of the Object
    of Interest. Explanan/ Independent Variable
  • Test your explanation against conventional wisdom
    and/or alternative explanations for your Object
    of Interest.

6
1. SPECIFYING THE RESEARCH QUESTION
  • PASSING THE SO WHAT? TEST
  • Does it contribute to the cumulative knowledge on
    the Object of Interest?
  • Does it Yield Any Theoretical Advances/
    Implications?
  • Does it Yield Any Practical/Substantive
    Implications?

7
2. PROPOSING CAUSAL EXPLANATIONS
  • Does X (Independent variable)
  • ? (Cause)
  • Y (Dependent variable)
  • Antecedent Variable Causally Prior to the
    Independent Variable
  • e.g. Religious Attitudes ? Attitudes on Abortion
    ? Presidential Vote Choice

8
2. PROPOSING CAUSAL EXPLANATIONS
  • Intervening Variable Causally Between the
    Independent and Dependent Variables i.e.,
    Conditioning or Contextual Factors
  • e.g. Presidential Election Year
  • ?
  • Divided Government
  • ?
  • Government Spending /or Taxes

9
3. FORMULATING HYPOTHESES(i.e., EDUCATED
GUESSES)
  • Derived From Either Theoretical Logic or
    Empirical Conjecture
  • Characteristics of a Good Hypothesis
  • An Empirical Statement
  • Generalization
  • Plausible
  • Specific
  • Appropriate
  • Testable

10
3. FORMULATING HYPOTHESES(i.e., EDUCATED
GUESSES)
  • Directional Relationship X has a positive
    impact on Y
  • Positive Relationship X and Y Covary in the Same
    Direction
  • Negative/Inverse Relationship X and Y Covary in
    the Opposite Direction

11
3. FORMULATING HYPOTHESES(i.e., EDUCATED
GUESSES)
  • Tautological Relationship A Hypothesis that
    contains two concepts (X Y) which are
    essentially identical.
  • e.g., As political insulation of bureaucratic
    agencies increases, these institutions act in a
    more independent fashion.

12
3. FORMULATING HYPOTHESES(i.e., EDUCATED
GUESSES)
  • Spurious Relationship When X appears to Cause Y,
    but actually Z is the true source of the causal
    relationship involving Y.
  • e.g., Conference Winning the Super Bowl Affects
    Stock Market Performance

13
3. FORMULATING HYPOTHESES(i.e., EDUCATED
GUESSES)
  • Endogenous (Two-Way) Relationship
  • When X ? Y, but Y ? X, too!
  • e.g., Democratization ? Economic Growth
  • Economic Growth ? Democratization

14
4. SPECIFYING UNITS OF ANALYSIS
  • Unit of Analysis Type or Level of Political
    Actors (or Policy) which the hypothesis is
    thought to apply.
  • E.G. 1 The impact of ideology on the
    legislators vote choice on a Budget bill.
  • Unit of Analysis Legislator

15
4. SPECIFYING UNITS OF ANALYSIS
  • E.G. 2 The impact of professionalized state
    legislatures on state economic growth.
  • Unit of Analysis Each State Legislator

16
5. ECOLOGICAL INFERENCE
  • EI Use of Aggregate Data to Study Individual
    Level Behavior.
  • EI should be avoided at all costs i.e., data
    should match the unit of analysis
  • WHY? Ecological Fallacy Problem
  • Erroneously claiming a relationship (or lack
    thereof) when one does not exist (or does exist)

17
6. CONCEPTS
  • The DNA of Empirical Inquiry
  • Needed! Operational Definitions
  • Conversion of Abstract Concepts into Measurable
    Concepts
  • PURPOSES
  • Transmissible ? Replication ? Extension
  • Empirical ? Inference ? Evidence

18
6. CONCEPTS
  • E.G. 1 ECONOMIC PERFORMANCE
  • Income Growth
  • Low Unemployment
  • Low Inflation
  • Trade Surplus
  • Exchange Rates

19
6. CONCEPTS
  • E.G. 2 SOCIAL ATTITUDES
  • Abortion
  • Race
  • Gay/Lesbian Marriage/Civil Unions
  • School Prayer
  • Guns/2nd Amendment

20
6. CONCEPTS
  • E.G. 3 REPRESENTATION
  • Descriptive Representation Personal
    Characteristics e.g., race, gender, religion
  • Symbolic Representation God and Country or
    Fighting for Working Americans - e.g.,
    speeches

21
6. CONCEPTS
  • Substantive Representation To what extent do
    elected representatives mirror the wishes of the
    majority of constituents (i.e., median
    constituent)?
  • e.g., legislators ideology
  • median constituent ideology

22
7. HYPOTHESIS TESTING
  • Scientific Generalization Expresses a
    Relationship Between Concepts.
  • Hypothesis An educated guess about
    relationships.
  • Well-Confirmed Hypothesis A hypothesis that is
    found to be true.
  • Laws Relationships confirmed 99-99.99999 of
    the time

23
7. HYPOTHESIS TESTING
  • Conditional Generalizations
  • Statistical Most or Tends (a generalization
    that relates to most of a population.
  • Universal All (a generalization that pertains
    to an entire population)

24
7. HYPOTHESIS TESTING
  • Characteristics of Generalizations
  • Empirical Import Grounded in real-world
    observation so that one must be able to confirm
    or deny relationships (i.e., falsifiable)
  • Systematic Import Concepts must be related in
    some meaningful manner (i.e., plausible
    relationship)

25
7. HYPOTHESIS TESTING
  • Null Hypothesis Assumption of no relationship
    (or difference) between variables.
  • Alternative Hypothesis Null hypothesis is false.
  • Directional Hypothesis An educated guess about
    the direction of the relationship (one-tailed
    hypothesis testing)

26
7. HYPOTHESIS TESTING
  • Non-Directional Hypothesis Null hypothesis is
    false but do not choose a direction for the
    relationship (two-tailed hypothesis testing)

27
7. HYPOTHESIS TESTING
  • Hypothesis Testing (Inferential) Errors
  • Type I Error Incorrectly rejecting the null
    hypothesis (i.e., falsely concluding existence of
    a relationship)
  • Type II Error Incorrectly accepting the null
    hypothesis (i.e., falsely concluding absence of a
    relationship)

28
8. MEASUREMENT
  • Measurement Systematic Observation and
    Representation by Quantitative Values (i.e.,
    Numbers)
  • Operational Definition of Concepts Deciding what
    kinds of empirical observations should be made to
    measure the occurrence of an attribute or
    behavior. These are important since different
    people mean different things by examining the
    same concept -- it is important to agree on some
    basic measure(s) of a concept so that some
    consistency occurs.

29
8. MEASUREMENT
  • E.G. 1 How to Measure the Ideology of Supreme
    Court Justices?
  • Rulings/Decisions Made on the SC
  • Expert Surveys of Legal Scholars
  • Justices Past Writings or Cases Prior to
    becoming a member of the SC

30
8. MEASUREMENT
  • E.G. 2 Measurement of U.S. Influence in the
    Middle East
  • Economic Trade
  • Economic Non-Military Aid (Humanitarian and
    Development)
  • Military Military Aid

31
8. MEASUREMENT
  • Operational definitions are seldom absolutely
    correct or incorrect but rather should be
    evaluated in terms of how well they correspond to
    the concept that is attempted to being measured.

32
8. MEASUREMENT
  • Factors that Plague Measurement
  • Properly Designed Instruments
  • e.g. Using different polls (with different
    formats) to make inferences about the same
    phenomenon.
  • Data Constraints
  • Analyzing data on Campaign Financing prior to
    1972 is impossible since Federal Election Laws
    did not require public disclosure

33
8. MEASUREMENT
  • Dependence on Secondary Sources
  • e.g. use of expert surveys or government
    documents which relies on the assessments of
    others.

34
8. MEASUREMENT
  • Levels of Measurement
  • Nominal Measurement A variable that assigns
    numerical values based upon discrete
    classification in mutually exclusive categories
  • e.g., gender, race, binary party affiliation,
    religion

35
8. MEASUREMENT
  • Ordinal Measurement More or Less comparisons
    can be made regarding different numerical values
    of a given variable. However, these values do
    not tell us anything about relative comparisons
    of how much more or less
  • e.g., candidate thermometer rankings,
    categorical assessments of education and icnome

36
8. MEASUREMENT
  • Interval Measurement Intervals between ordinal
    categories/values has meaning. That, is, we can
    assess how much larger or smaller in precise
    terms. Rankordered items, but places equal
    intervals between its categories. It cannot make
    statements such as candidate is twice as popular
    as candidate Y since there is no absolute zero
    point.
  • e.g., inflation, budget deficits

37
8. MEASUREMENT
  • Ratio Measurement Most complete form of
    measurement that states the exact magnitude
    differences among categories by having the same
    properties of interval measurement, plus absolute
    comparisons based upon zero baseline.
  • e.g., vote share in an election,
  • unemployment rate

38
8. MEASUREMENT
  • Reliability The extent to which a measure yields
    the same results on repeated trials/samples from
    a population.
  • Goal Ensure consistency among results
  • from different trials

39
8. MEASUREMENT
  • Different Methods for Analyzing Reliability
  • Test-Retest Method Applying the same "test"
    (i.e., research instrument(s) / question(s)) to
    the same observations after a period of time and
    comparing the results of the different
    measurements
  • (e.g., SAT example)
  • Drawback Potential for Contagion between
    measurements taken at two different points in
    time (e.g., learning effects)

40
8. MEASUREMENT
  • Alternative Form Method Using two different
    measures to gauge the same concept at two
    different points in time.
  • (e.g., two surveys on policy liberalism that
    have different questions on them).
  • Drawback Potential for Contagion between
    measurements taken at two different points in
    time as well as a lack of comparability
    problem between using different measures of the
    same concept

41
8. MEASUREMENT
  • Split-Halves Method Assesses two measures of the
    same concept simultaneously. The results of the
    two measures are compared. (e.g., half of
    liberalism questions are given to one group while
    the remaining half are given to another group).
  • Drawback While it overcomes the temporal
    contagion problem, it requires that the two
    subgroups are representative of one another
    (i.e., the means of breaking down the sample into
    two groups is unbiased

42
8. MEASUREMENT
  • Validity The extent to which a measure is
    representing what it is supposed to measure.
  • Goal Ensure accurate measurement of concepts by
    exhibiting a strong association between the
    measure and the related concept.

43
8. MEASUREMENT
  • The validity of a measure is more difficult to
    demonstrate empirically than its reliability
    because it is difficult to obtain information
    regarding the correspondence between the
    measurement of a concept and the actual presence
    or amount of the concept itself

44
8. MEASUREMENT
  • Different Methods for Analyzing Validity
  • Face Validity Does it pass the smell test? A
    matter of judgment, not empirical proof
  • Drawback Unless consensus exists for a
    particular measure of a given concept, it is
    difficult to establish face validity

45
8. MEASUREMENT
  • Content Validity involves determining the full
    domain or meaning of a particular concept and
    then making sure that measures all portions of
    the domain are included in the measuring scheme.
    (e.g., measuring the full domain of policy
    liberalism by the American public)
  • Drawback Must ensure that full domain of a
    particular concept is both defined and accounted
    for in the measurement scheme (difficult reaching
    agreement on this matter)

46
8. MEASUREMENT
  • Construct Validity When a measure of a concept
    is related to a measure of another concept with
    which the original concept is thought to be
    related (e.g., strength of partisan
    identification and voting behavior Freshman
    undergraduate GPA and SAT scores).
  • Drawback Failure to establish a relationship
    could pertain to many things (i.e., theoretical
    relationship is in error, poor measures of a
    concept, or inappropriate testing procedures)

47
8. MEASUREMENT
  • Interitem Association relies on the similarity
    of outcomes of more than one measure of a concept
    to demonstrate the validity of the entire
    measurement scheme. (i.e., a multivariate analog
    to assessing construct validity)
  • Drawback Same as for Construct Validity, except
    less problematic given reliance on multiple
    measures as a means to assess valid measurement
    of a concept

48
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49
9. RESEARCH DESIGN
  • ELEMENTS OF CAUSALITY IN A RESEARCH DESIGN
  • Covariation Does X covary with Y?
  • Time Order Does X precede Y?
  • Confounding Factors Do A B also cause Y so as
    to make X moot?

50
9. RESEARCH DESIGN
  • Example The impact of race on voting Y Vote
    Choice XRace ZPartisan ID

51
9. RESEARCH DESIGN
  • Experimental Research Designs
  • Researcher has control over stimulus applied to
    the experimental group
  • Researcher controls assignment of subjects into
    experimental and control groups i.e.,
    randomization
  • Observe/Measure responses or behavior

52
9. RESEARCH DESIGN
  • Researcher does not have to control for
    extraneous factors (Zs)

53
9. RESEARCH DESIGN
  • (1) Classic PreTest and PostTest Design
  • R? Pre-Test E ? X ? Post-Test E
  • R? Pre-Test C ? P ? Post-Test C
  • Measurement of the dependent variable are taken
    both before and after the treatment.
  • This allows one to examine differences that exist
    between the groups before the treatment in order
    to see whether these differences are attributable
    to the treatment, or inherent group differences.

54
9. RESEARCH DESIGN
  • WEAKNESS Only a single group receives a
    pretest, a stimulus, and a posttest. One
    problem with this method is that there is no sure
    way of knowing whether the change in the
    dependent variable was due to the experimental
    factor and not to other factors. Also, there is
    no way to check for pretest and posttest based
    stimulus interaction.

55
9. RESEARCH DESIGN
  • Simple Post-Test Design
  • Two groups (E C) and two variables (X Y)
  • R ? E ? X ? Post-Test E
  • R ? C ? P ? Post-Test C
  • Weakness Random Assignment is more Uncertain
    since no pre-testing occurs

56
9. RESEARCH DESIGN
  • Repeated Measurement Design
  • Multiple Pre-Test and Post-Test measurement
    with both groups being administered the stimulus
    at the same time.
  • E.G. Focus groups viewing a presidential debate.

57
9. RESEARCH DESIGN
  • Multi-Group Experimental Design
  • Akin to the Classic Pre-Test and Post-Test
    Design, except with multiple experimental groups
    and altering the stimulus treatments.
  • Three Group Example
  • R ? Pre-Test E1 ? X ? Post-Test E1
  • R ? Pre-Test E2 ? X ? Post-Test E2
  • R ? Pre-Test E3 ? X ? Post-Test E3
  • R ? Pre-Test C ? P ? Post-Test C

58
9. RESEARCH DESIGN
  • Strength Overcomes the weakness associated with
    Classic PreTest and PostTest Design

59
9. RESEARCH DESIGN
60
9. RESEARCH DESIGN
  • Field Experiments experimental design in a
    natural settings, whereby the researcher cannot
    randomly assigns subjects to experimental and
    control groups, but can manipulate the
    experimental variable.

61
9. RESEARCH DESIGN
  • One cannot control for non-experimental factors
    relating to extraneous factors (e.g., historical
    effects) that naturally occur outside the lab.
  • Pro Improves external validity from being in
    real world setting
  • Con Lowers internal validity ? spurious effects
    more likely

62
9. RESEARCH DESIGN
  • E.G. Voter Turnout Do Early Voting Rules
    Improve Political Participation?
  • NO Random Assignment of Eligible Citizens to
    Early Voting (experimental) and Election Day
    Voting (control) Groups within a state that
    allows for early voting.
  • Researcher controls who votes early and who
    votes on election day

63
9. RESEARCH DESIGN
  • Need to examine a state with early voting laws
    for comparability purposes
  • Must select a common feature(s) so that the
    groups mirror one another. (e.g., college age
    voters representing as the population of
    interest)
  • Must be concerned about environmental
    influences that may lower internal validity

64
  • NON-EXPERIMENTAL DESIGNS

65
9. RESEARCH DESIGN
  • Case Study Design Small-N Design (single,
    comparative, or focus group)
  • In-depth investigation of one or a handful of
    observations
  • Most often used for exploratory or descriptive
    purposes

66
9. RESEARCH DESIGN
  • Cons Limited Generalizability Sample Selection
    and Spurious Relationships
  • Pro Deep Understanding of Causality

67
9. RESEARCH DESIGN
  • Cross-Sectional Design Measurements of both the
    independent and dependent variables taken at
    approximately the same time
  • Individuals (surveys) or Aggregate (groups,
    institutions, states, nations)

68
9. RESEARCH DESIGN
  • Pros Improves External Validity due to large N
    -- especially generalizability across populations
  • Cons Lowers Internal Validity (i.e., determining
    true cause and effect)

69
9. RESEARCH DESIGN
  • E.G. Early Voting Laws and Vote Choice in 2008
    Presidential Election
  • Pro A sample of thousands of voters compare
    individuals from early voting to non early
    voting states
  • Con Do not have pre-test observations on the
    impact of switching to early voting laws.

70
9. RESEARCH DESIGN
  • Time Series (Longitudinal) Design
  • Repeated Measurement for a single cross-sectional
    unit through time
  • Pros (1) Cause?Effect (2) Dynamic Effects
  • Cons (1) Omitted Factors
  • (2) Period/Transitory effects

71
9. RESEARCH DESIGN
  • E.G., Stimsons Policy Mood Measure
  • Did Reagan Lead the Republican Revolution?

72
9. RESEARCH DESIGN
  • Interrupted Time Series Design
  • Analogous to a Classic Pre-Test / Post-Test
    Design
  • Researcher does not have control over group
    assignment nor application of stimulus

73
9. RESEARCH DESIGN
  • Con Threat to Internal Validity due to omitted
    factors that may drive change independent of the
    intervention event
  • Pro Analysis f dynamic political change
  • E.G., Mass Voter Mobilization of
    African-Americans in the American South

74
9. RESEARCH DESIGN
  • Panel Design measurements both cross-section and
    through time. Change in individuals or aggregate
    units through time
  • Pro Overcomes Internal Validity problems
    associated with Cross-Sectional Design
  • Con Panel Mortality

75
9. RESEARCH DESIGN
  • E.G. Cross-National Relationship between
    Democratization and Economic Growth

76
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77
10. SAMPLING
  • What is a Sample? A subset of observations/units
    derived from a population.
  • What is a Population? A well-defined set of
    observations that encompasses a particular
    hypothesis
  • The Costs of Population Analysis time, money,
    etc

78
10. SAMPLING
  • The Basics of Sampling
  • Sample Statistics () Statistics derived from a
    sample used to approximate corresponding
    population values/ parameters (e.g., mean,
    median, variance/standard deviation)
  • Sample Statistic

79
10. SAMPLING
  • How well does the sample estimate approximate
    population parameter?
  • Sample Bias Bias attributable to systematic
    exclusion of elements from a sample
  • Sample Bias (Desirable)

  • (Undesirable)

80
10. SAMPLING
  • E.G., Sampling Bias in Election Polling (Likely
    Voters vs. Registered Voters)

81
10. SAMPLING
  • Sampling Error The amount of error attributable
    to a sample estimate
  • Sampling Error ,
  • where the expected (i.e., average) value of the
    sample estimator equals the corresponding
    population parameter

82
10. SAMPLING
  • Elements of Statistical Inference
  • Expected Value the average (long-run) value of a
    sample statistic based on repeated samples from a
    population
  • Standard Error the measure of dispersion (i.e.,
    standard deviation) of a sampling distribution
    surrounding the expected value of the sample
    estimator ( )
  • Confidence Interval (Sampling Distribution) What
    of the time that we observe the sample
    statistic ( ) if we were to replicate the
    sample k times.

83
10. SAMPLING
  • Confidence Interval Equation
  • 90 C.I.
  • 95 C.I.
  • 99 C.I.
  • Note The scalars above assume a Standard Normal
    Probability Distribution.

84
10. SAMPLING
  • How Large A Sample?
  • As sample size ?, Sampling Error ?
  • Cost ?
  • See Table 7.4 in Textbook (p. 237) for details.

85
10. SAMPLING
  • Probability Samples Each element in the
    population has a known probability of being
    selected.
  • Simple Random Sample each element has an equal
    chance of selection
  • Systematic Sample Elements are selected at
    predetermined intervals (as opposed to at random)

86
10. SAMPLING
  • Stratified Sample Elements share one or more
    characteristics are grouped (e.g., gender, race,
    religion), and elements are selected from each
    group in proportion to each groups proportion in
    the total population.
  • Cluster Sample Used to circumvent not having a
    list of elements in the sample population. Use
    only a partial list of elements.

87
10. SAMPLING
  • Non-Probability Samples Each element in the
    population has an unknown probability of being
    selected.
  • Purposive Sample Researcher selects cases of
    interest

88
10. SAMPLING
  • Convenience Sample inclusion of elements based
    upon ease
  • Quota Sample Elements selected based upon their
    proportion to their representation to the
    population (nonprobability sampling analog to
    proportionate stratified sampling)
  • Snowball Sample elements are chosen through
    word of mouth contact by other elements

89
  • END OF UNIT 2 MATERIAL
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