Transcript Thinking Like a Psychologist

Thinking Like a Psychologist
Part III
Evaluating the Research
• Each research study makes its own contribution
• More importantly, how does each study relate to
the others and to the theory being tested?
• Connectivity = old + new
– A good adaptation to a theory not only explains the
new findings but accounts for previous findings
Evaluating the Research
• Don’t jump to conclusions!!!
– Media is notorious for doing this
• Previous findings are not to be discarded
because of one new finding.
– Replication, replication, replication
– Evaluate the methods used
• Is someone trying to say correlation =
causation?
Evaluating the Research
• Converging Evidence
– Multiple research studies reveal similar findings.
• Convergent Validity
– Measures that are predicted to be related, are.
• Diverging Evidence
– Research that is contrary to the typical findings.
• Divergent Validity
– Measures that are predicted to be unrelated, aren’t.
Evaluating the Research
• When designing research:
– Read the available literature
– Examine the shortcomings (addressed and
unaddressed)
– Review researcher recommendations
– Note the variables examined
• Is there another angle to examine?
– Note the predominate findings and
explanations
Evaluating the Research
• Meta-analysis
– Compilation of multiple research findings from
multiple studies examining the same research
question
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Find all relevant articles
Evaluate the articles for inclusion
Code the data to be included
Examine the outcomes
Evaluating the Research
• Events and phenomena typically do not
have one cause.
• Although a research study may only
examine one or two variables, that does
not mean those the only ones involved.
– Multiplicity of Causation
– Interaction Effects
Evaluating the Research
• Sample size is important in evaluating the
results
– n = 25 vs. n = 300
– A smaller sample size is not as representative
of the population
– Increasing sample size and number of
samples, decreases error.
Samples and Populations
• Central Limit Theorem
– As sample size increases, the distribution
approaches normal
– Mean of the sample means is equivalent to
the population mean
– Standard deviation of the sample means is
the standard error
The math:
9 + 5 + 12 + 22 +
19 + 16 + 13 + 10
Sample1
M=5
Sample 3
M = 12
Sample 4
M=9
Sample 8
M = 19
=106
Population X
µ = 15
Sample 7
M = 16
Sample 6
M = 22
Sample 5
M = 10
Sample 2
M = 13
106/8 = 13.25
Central Tendency
• Mean, Median, Mode
– Mean
• All of the scores summed and divided by the
number of scores (M = Σx/n); a.k.a. the average
– Median
• The values separating the upper half of the scores
from the lower half.
– Mode
• The score that occurs most frequently