how do we identify causes? the criteria of causation © pine forge press, an imprint of sage...

How Do We Identify Causes?

The criteria of causation

© Pine Forge Press, an imprint of Sage Publications, 2006

• empirical association

• temporal priority of the independent variable

• nonspuriousness

• identifying a causal mechanism


Empirical Association

The independent variable and the dependent variable must vary together.

A change X is associated with a change in Y.


X Y

Temporal Priority of the

Independent Variable

The change in X must occur before the change in Y.


X Y t1 t2

Nonspuriousness

We say that a relationship between two variables is spurious when it is due to variation in a third variable; so what

appears to be a direct connection is in fact not.


Y X

Z

X

A Causal Mechanism

A causal mechanism is the process that creates the connection between variation in an independent variable

and the variation in the dependent variable it is hypothesized to cause


False Criteria for Nomothetic Causality

• Research can determine some causes, but it cannot determine complete causation.

• Exceptions do not disprove a causal relationship.

• Causal relationships can be true even if they don’t apply in a majority of cases.

The design of your study will help you identify causality

Keeping all of this straight will help you make stronger and more

interesting arguments about your findings.

Research Design

Time Dimension

• Cross-Sectional Studies

• Longitudinal Studies– Trend-Repeated Cross-sectional– Cohort-Event-based design– Panel-Fixed sample

Nonspuriousness

• Random assignment

• Control Group

• Statistical controls– Multivariate Regression

• Including control variables (which ones? – theory)

A Causal Mechanism

How do you identify a causal mechanism?

1. Theory

2. Prior Literature

3. Logic


EXPERIMENTS


Different Types ofExperimental Design

• true experiments• quasi-experiments• evaluation research• nonexperimental designs


True Experiments

True experiments must have at least three things:•Two comparison groups (in the simplest case, an experimental and a control group)

• Variation in the independent variable before assessment of change in the dependent variable

•Random assignment to the two (or more) comparison groups


True experiments must have at least one experimental group (subjects who receive some treatment) and at least one comparison group (subjects to whom the experimental group can be compared).

True Experiments

01

01


All true experiments have a posttest—that is, measurement of the outcome in both groups after the experimental group has received the treatment. Many true experiments also have pretests that measure the dependent variable prior to the experimental intervention. A pretest is exactly the same as a posttest, just administered at a different time.

True Experiments

01

01


True ExperimentsRandomization, or random assignment, is what makes the comparison group in a true experiment such a powerful tool for identifying the effects of the treatment. A randomized comparison group can provide a good estimate of the counterfactual—the outcome that would have occurred if the subjects who were exposed to the treatment actually had not been exposed but otherwise had had the same experiences (Mohr, 1992:3; Rossi & Freeman, 1989:229).


Quasi-experiments

A quasi-experimental design is one in which the comparison group is predetermined to be

comparable to the treatment group in critical ways, such as being eligible for the same services or

being in the same school cohort.•Nonequivalent control group designs have experimental and comparison groups that are designated before the treatment occurs and are not created by random assignment.

•Before-and-after designs have a pretest and posttest but no comparison group. In other words, the subjects exposed to the treatment serve, at an earlier time, as their own controls.


Quasi-experiments

Ruth Wageman (1995) used a quasi-experimental design to investigate how the way tasks were designed and rewards allotted affected work team functioning.

Her research question was whether it was preferable to organize work tasks and work rewards in a way that stressed team interdependence or individual autonomy.


Quasi-experiments

David P. Phillips’s (1982) study of the effect of TV soap-opera suicides on the number of actual suicides in the United States illustrates a more powerful multiple group before-and-after design.


Nonexperimental Designs

Cross-sectional designs, termed “one-shot case studies” in the experimental design literature, are easily able to establish whether an association exists between two variables, but we cannot be anywhere near as confident in their conclusions about appropriate time order or nonspuriousness as with true experiments or even quasi-experiments.

Longitudinal designs improve greatly our ability to test the time order of effects, but they are unable to rule out all extraneous influences.


Causal (Internal) Validity

• When characteristics of the experimental and comparison group subjects differ

• When the subjects develop or change during the experiment as part of an ongoing process independent of the experimental treatment.

• When something occurs during the experiment, other than the treatment, which influences outcome scores.

• When either the experimental group or the comparison group is aware of the other group and is influenced in the posttest as a result (Mohr, 1992).

There are four basic sources of noncomparability (other than the treatment) between a comparison group and an experimental group.


External Validity

Researchers are often interested in determining whether treatment effects identified in an experiment hold true for

subgroups of subjects and across different populations, times, or settings.

There is always an implicit tradeoff in experimental design between maximizing

causal validity and generalizability. The more that assignment to treatments is randomized

and all experimental conditions are controlled, the less likely it is that the research subjects and setting will be

representative of the larger population.© Pine Forge Press, an imprint of Sage Publications, 2006

Independent Sample T-test

• Classical design used in psychology/medicine • N subjects are randomly assigned to two

groups (Control * Treatment). • After treatment, the individuals are measured

on the dependent variable. • A test of differences in means between groups

provides evidence for the treatment's effect.

Measures of Variation

• A lot of statistical techniques (using interval data) use measures of variation in some manner

• What is the difference between a standard deviation, the standard error of the mean, and the standard error of the difference between means? Or How are they related? Look in the glossary to help you answer these questions?

Using Measures of Variation

• Leaned how to measure variation in data, i.e., variance, standard deviation (Ch.4)

• Used the normal curve & SD to calculate z-scores and probabilities (Ch.5)

• Used the normal curve & the z-score & the SE of the mean to calculate confidence intervals (Ch.6)

• Used the concept of the confidence interval and the standard error of the differences between means to calculate the t-test (Ch.7)

• Use the sum of squares Σ(X – Mean)2 [sum of the squared differences from the mean] in ANOVA

Null Hypothesis

• The two groups come from the same population or that the two means are equal

• μ1 = μ2

Levels of Significance

• What does an α = .05 level of significance mean?

• We decide to reject the null if the probability is very small (5% or less) that the sample difference is a product of sampling error.

• The observed difference is outside the 95% confidence interval of the difference

Choosing a Level of Significance

• Convention• Minimize type I error – Reject null

hypothesis when the null is true• Minimize type II error – fail to reject null

when the null is false• Making alpha smaller reduces the

likelihood of making a type I error• Making alpha larger reduces the

probability of a type II error

Independent Sample T-test Formula

21

21

21

222

211

221 NN

NN

NN

sNsNs xx

t =

21

21

XXs

XX

Assumptions of the t-test

• 1. All observations must be independent of each other (random sample should do this)

• 2. The dependent variable must be measured on an interval or ratio scale

• 3. The dependent variable must be normally distributed in the population (for each group being compared). (NORMALITY ASSUMPTION) [this usually occurs when N is large and randomly selected]

• 4. The distribution of the dependent variable for one of the groups being compared must have the same variance as the distribution for the other group being compared. (HOMOGENEITY OF VARIANCE ASSUMPTION)

Don’t worry about these assumptions to much, but

• Point 1: statistical tools are attempting to quantify and analyze very complex social/political phenomenon

• Point 2: For these test to be accurate they relay on simplifying the world with many assumptions that might not be true

• Point 3: social science researchers violate these assumptions quite often, but try to be honest about it

• Point 4: there are sometimes ways of testing and adjusting for violations

SPSS & the Independent Sample T-Test

Independent Samples Test

.073 .789 .288 18 .777 4.00000 13.91242 -25.22892 33.22892

.288 17.971 .777 4.00000 13.91242 -25.23230 33.23230

Equal variancesassumed

Equal variancesnot assumed

VAR00001F Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Group Statistics

10 102.0000 31.72801 10.03328

10 98.0000 30.47768 9.63789

VAR000021.00

2.00

VAR00001N Mean Std. Deviation

Std. ErrorMean

how do we identify causes? the criteria of causation © pine forge press, an imprint of sage...

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