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texts
any kind of communication that can be documented
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Cohen's Kappa
- K = #agree/#possible - 1/n
- _______________________
1-1/n
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minimum acceptable inter-relater reliability
>.70
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what causes low inter-relater reliability?
- 1. definition problems
- 2. coding rules
- 3. coder drift
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6 types of texts
- 1. public/private
- 2. mediated/non-mediated
- 3. scripted/unscripted
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How do we increase inter-rater reliability?
- 1. good definitions
- 2. clearly defined coding rules
- 3. test inter-relater reliability often
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3 criteria for good coding system
- 1. mutually exclusive
- 2. exhaustive
- 3. equivalent
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strengths of content analysis
- - less biased & more comprehensive than surveys
- -unobtrusive
- -less expensive to make errors
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weaknesses of content analysis
- -limited to recorded information
- -validity problems
- -coding can be too simplistic
- -assuming there is a uniform relationship between symbols and meaning
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How are experiments unique?
only method that implies causality
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What are the requirements for inferring causality?
- 1. IV + DV are correlated
- 2. IV comes before DV
- 3. Rule out alternative explanations
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strengths of experiments
- - ability to isolate the experimental variable and effect over time
- -experiments don't involve large number of subjects and can be replicated many times
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weaknesses of experiments
- -artificiality
- -little likelihood situations will occur in real life
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Double - Blind
neither experimenter nor anyone who comes in contact with the participants has knowledge of whether they are in control group or experimental group
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Control group
subject to exact same conditions as experimental group, except without the IV
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random group assignment
- each participant has an equally likely chance of being in control or experimental group
- gives assurance that each group is equivalent
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matching
rather than random assignment, experimenters predict what variables might affect the DV and account for those in the group assignment
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pretesting/posttesting
- testing the DV before and after IV is administered
- gives researchers a baseline to compare effects of IV
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manipulation check
asks participants after the study if the independent variable actually meant what it was intended to
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experimenter effect
experimenter unconsciously treats subjects differently if they know the purpose of the study, affecting how participants behave
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observer bias
if researchers/observers know which subjects are in experimental/control group, what they see may be biased
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researcher attribute effect
traits of the researcher affect behavior/data of subjects
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Hawthorne effect
participant's responses are influenced because they are aware of being observed
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Testing effect
participants are primed by the test to respond in a certain way
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maturation effect
subjects naturally change over time during the study regardless of the independent variable
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experimental mortality effect
participants drop out before the study is complete
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selection bias effect
when subjects are allowed to choose their groups, the groups will not be the same to begin with
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intersubject bias
participants influence one another's behaviors
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compensatory rivalry
one group compensates for missing experimental condition by working harder
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demoralization
sense of not progressing causes control group to give up
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History effect
a historical effect occurring outside the study affects the outcomes
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instrumentation
unreliable scale yields unreliable results
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statistical regression
outliers/extremes will naturally test closer to mean on posttest
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Maxi-con-mini
- maxi: maximize IV
- con: control for third/extraneous variables
- mini: minimize error variance
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How do researchers minimize the weaknesses of experiments?
- - make conditions the same for control & experimental group
- -double-blindness
- -random selection
- -manipulation check
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Solomon 4
- 2 control and 2 experimental groups
- 1 experimental and 1 control are pretested
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alpha level
significance of the probability that the stats are due to sampling error, measures statistical significance
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standard deviation
measures dispersion of statistics, higher =more dispersed data
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correlation
the extent to which two variables are related, the extent of agreement/consistency, shows reliability of results
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t-test
examines the difference between two groups, will two groups with same IV vary on DV?
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