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Tam Aligaz

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Title: Tam Aligaz

1
Tam Aligaz

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Topics

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Internal Validity

Validity in general refers to the approximate
truth of propositions, inferences, or
conclusions.
What is internal validity?
Its the approximate truth about inferences
regarding cause-effect or causal relationships
Only relevant in studies that try to establish a
causal relationship
Uses
For studies that assess the effects of social
programs or interventions

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External Validity

External Validity
Refers to the approximate truth of conclusions
that involve generalizations
The degree to which the conclusions in your study
would hold for other persons in other places and
at other times
Totally related to generalizing (ability to
generalize from our study context to other
people, places or times )

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Example of Internal Validation

For instance, if program X causes Y
observation, then it means what we did with
program X caused what we observed in Y to
happen

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Causal Relationship

How to establish causal relationship?
Generally, there are three criteria that we must
meet before we can say that we have evidence for
a causal relationship
Temporal Precedence
Covariance of the Cause and Effect
No plausible alternative explanation

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Temporal Precedence

E.g. Does inflation cause unemployment?
In this kind of cyclical situation involving
ongoing processes that interact that both may
cause and, in turn, be affected by the other.

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Covariation of the Cause and Effect

Before we can show that we have a causal
relationship we have to show that we have some
type of relationship
E.g. if X then Y if not X then not Y
Whenever X is present, Y is also present, and
whenever X is absent, Y is too. Therefore there
is a relationship between X and Y

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No Plausible Alternative Explanations

Just because we show there's a relationship
doesn't mean it's a causal one. It's possible
that there is some other variable or factor that
is causing the outcome.
This is sometimes referred to as the "third
variable" or "missing variable" problem

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Threats to Internal Validity

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Single Group Threat

Let's consider two single group designs and then
consider the threats that are most relevant with
respect to internal validity.

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Multi Group Threat

There really is only one multiple group threat to
internal validity that the groups were not
comparable before the study.
selection bias or selection threat.
A selection threat is any factor other than the
program that leads to posttest differences
between groups

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Social Threat

The social threats to internal validity refer to
the social pressures in the research context that
can lead to posttest differences that are not
directly caused by the treatment itself.
Most of these threats occur because the various
groups, or key people involved in carrying out
the research are aware of each other's existence
and of the role they play in the research project
or are in contact with one another
Many of these threats can be minimized by
isolating the two groups from each other

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Sampling

Sampling is the process of selecting units (e.g.,
people, organizations) from a population of
interest
By studying the sample we may fairly generalize
our results back to the population from which
they were chosen

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Statistical Sampling Terms

Response
Its a specific measurement value that a sampling
unit supplies
Parameter
If we measure the entire population and calculate
a value like a mean or average, its called
parameter of the population
Sampling distribution
The distribution of a statistics across an
infinite number of samples
Sampling error
Standard error is called sampling error which
gives some idea of the precision of our
statistical estimate

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Probability Sampling

A probability sampling method is any method of
sampling that utilizes some form of random
selection
In order to have a random selection method, we
must set up some process or procedure that
assures that the different units in your
population have equal probabilities of being
chosen
These days, computers are used as the mechanism
for generating random numbers as the basis for
random selection.

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Non Probability Sampling

The difference between nonprobability and
probability sampling is that nonprobability
sampling does not involve random selection and
probability sampling does.
Nonprobability samples cannot depend upon the
rationale of probability theory.
With a probabilistic sample, we know the odds or
probability that we have represented the
population well and are able to estimate
confidence intervals for the statistic.
With nonprobability samples, we may or may not
represent the population well, and it will often
be hard for us to know how well we've done so