Task 7 Cause and effect Flashcards
Effect
is the outcome e.g. learning together increases productivity so increased productivity is the effect
Cause
is the reason of the effect e.g. learning together increases productivity e.g. learning together is the cause
Covariation
not sure about a link between the two variables. They must occur together but you don’t know the relationship
Third variable problem
The possibility that correlational relationships may result from the action of an unobserved third variable
→may influence both of them, causing them to vary together even there is no direct relationship between them
Directionality Problem
direction of causality is sometimes difficult to determine (causal arrows, what is predictor and what is criterion value)
independent variable
Whos values and are chosen and set by the experimenter
dependent variable
the variable you want to meassure
Extraneous variables
are those that may affect the behaviour but are not of interest for the present experiment, so you can’t investigate them
Precedence
the hypothesized causal variable must reliably precede the effect variable.
Exclusion of alternative explanations
other explanations for the observed covariation must be reasonably excluded.
Logical mechanism
there must be a plausible account for the hypothesized causal relation.
Causal relationship
one variable directly or indirectly influences another (changes in the value of one variable directly or indirectly cause changes in the value of a second)
→can be unidirectional A influences B but not other way around (e.g. brick on your toe leads to screaming)
→can also be bidirectional, each variable influences another
Correlational relationship
changes in one variable accompany in another, but the proper tests have not been conducted to show that either variable causes changes in the other
→when changes in one variable are accompanied by specific changes in another, the two variables are said to covary
Demonstrating
exposes the group to only one treatment condition
Confounding variable
Two variables are confounded when their effects on a response variable cannot be distinguished from each other. The confounded variables may be either explanatory variables or lurking variables or both
→damages internal validity, you may not be able to establish a casual relationship between your independent variables and your dependent
conceptual confound
It may due to overlapping meaning between to variables (correlation between outcome and another outcome→ makes no sense) e.g. being sad leads to depression is wrong because the one is actual part of depression
Quasi-Independent variable
in an experimental design, any of the personal attributes, traits, or behaviours that are inseparable from an individual and cannot reasonably be manipulated (e.g. gender age and ethnicity)
Quasi-experiment
Tries to isolate a causal influence by selection rather than manipulation. Rather than causing X to vary, we select cases in which X does vary
between subject design
divide participants in two or more groups, and these groups receive different values of the variable that is of interest
Simpsons paradox
As is the case with quantitative variables, the effects of lurking variables can strongly influence relationships between two categorical variables
→An association or comparison that holds for all of several groups can reverse direction when the data are combined to form a single group. This reversal is called Simpson’s paradox
common response
When the observed association between the variables x and y is explained by a lurking variable z
correlational design
examine the relationship between two or more variables
→cross-sectional in which all observations are made at one time
→longitudinal: measurements are made at to or more time points
Mediator variable
are variables that are between cause and effect so A leads to D (mediator variable) which leads to B
conceptual confound
It may due to overlapping meaning between to variables
Moderator variable
there is only a association between your variables under specific circumstances these are the moderators (e.g. people buy more when they are hungry this is only right for impulsive people, impulsiveness is the moderator)
No treatment group (control group)
receives no treatment is used as base/control group
Placebo control groups
- Outcome research: simply investigates the effectiveness of a treatment
- Process research: attempts to identify the active component of a treatment. In process research, it is essential that the placebo effect be separated from other, active components of the treatment
Placebo
psychosomatic) The mind (psyche), rather than the placebo itself, has an effect on the body (somatic). It occurs simply because the participants think the medication is effective
Development design
o Cross generational: Take different age groups
o Longitudinal: Take one group and observe them over a long time
Sources of Confounding
History: specific events other than treatment occur between observations
• Maturation: fatigue or aging change performance over time
• Instrumentation: instrument calibration changes and confounds the effect
• Statistical regression: subjects selected for treatment on the basis of their extreme scores tend to move closer to the mean when retesting
• Biased subject selection: nonrandom • Experimental mortality: dropouts • Demand characteristics: participants know the purpose of the study and behave differently
