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researcher simply observes and describes behavior (ex jane goodall)
best use: to answer questions not involving the relationship between variables |
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| a reserach technique in which the researcher determines relationship between variables without manipulating them |
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| The variable in an experiment for which the researcher chooses values |
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| the variable the researcher measures to determine the effects of the IV |
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| A research design in which each subject is assigned to only ONE level of the IV |
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| A research design in which each subject is assigned to ALL levels of the IV |
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| Advantage of experimental approach (vs quasi & nat. observational) |
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| In contrast to all other research techniques, an experiment allows the researcher to infer a CAUSAL relationship between IV and DV |
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A research study that has the following criteria:
1. random assignment
2. researcher manipulates the IV |
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| Occurs when every member of the population to which we would like to generalize the results has an equally likely chance to participate in the research |
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| once the participants for the experiment have been chosen, random assignment occurs where each participant has an equally likely chance to be assigned to each IV level in a between subjects design, or to each treatment order in a within subjects design |
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| A research technique in which the researcher manipulates the IV but which FAILS to have random assignment |
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| a research technique that does NOT allow the researcher to infer causation |
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| Circumstances (3) when an experiment CANNOT be done |
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1. Difficult or impossible to manipulate the IV
2. Might be unethical to manipulate the IV
3. Subjects cannot be randomly assigned |
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| Levels of the independent variable |
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| the specific values of the IV the researcher chooses to use in the study |
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| an explanation for a phenomenon that can be falsified and that involves entities that cannot be directly observed |
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| A tentative statement about the possible relationship between observable variables |
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| when is the median a more useful descriptive statistic of a distribution? |
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| When the distribution is skewed |
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| formula for the sum of squares: |
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if a then b, b therefore a
ex: if you brush your teeth, orange juice tastes gross. orange juice tastes gross, therefore you must have brushed your teeth.
THIS IS A FALLACY. The orange juice can just taste gross. |
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| a graph showing the number of times each score occurred in a data set |
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| a symmetrical, bell-shaped distribution |
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| Positively skewed distribution |
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| a distribution with a few extreme high scores (remember: Frequency(Y-axis) vs score: (x-axis) |
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| negatively skewed distribution |
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| a distribution with a few extreme low scores (remember: frequency= Y axis, score= x axis) |
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| what do standardized scores allow, and what is the formula for it? |
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| S= √[ (X-Xbar)² / (N-1)] Allows comparisons to be made between scores measured on different scales by placing all scores on a common scale |
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| when do we use a correlational approach? |
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Definition
1. When manipulating the variables is difficult or impossible
2. " would be unethical |
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| Properties (4) of scales of measurement |
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Definition
Identity- Identity occurs when different entities receive different scores
Magnitude: occurs when the ordering of the values reflects the ordering of the trait being measured
equal intervals: occurs when a difference of 1 on the scale means the SAME AMOUNT everywhere on the scale
Absolute zero: occurs when a score of zero indicates complete absence of the trait being measured |
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| the falsifiability criterion for theroies |
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Definition
For a theory to be useful, it must be able to generate predictions - ie some hypothetical facts that the theory is unable to explain
(in other words, it must be theoretically possible to falsify) |
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" If P implies Q and P is true, then Q is true"
Valid form of argument but not useful to science because it assumes theory is true |
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"If p is true, q is true. Q is true
Therefore, p is true"
Invalid form of argument because a correct prediction does not prove the theory to be true (it is logically impossible to prove a theory true) |
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"If p is true, then q is true
Q is not true, Therefore, p is not true"
Both valid and useful to science because it can be used to prove theories false |
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| A causal relationship exists between two variables if a change in one results in a change in the other |
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| Occurs when every member of the population to which we would like to generalize the results has an equally likely chance to participate in the research |
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| scales of measurement (4) |
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Definition
-nominal
-ordinal
-interval
-ratio |
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| Scale of measurement: nominal (properties) |
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nominal scales have only the identity property
NO ARITHMETIC OPERATIONS ARE MEANINGFUL TO NOMINAL SCALES
(ex. Football jersey numbers) |
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| scale of measurement: ordinal (properties) |
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Definition
Has only the Identity AND Magnitude properties
NO ARITHMETIC OPERATIONS ARE MEANINGFUL TO ORDINAL SCALES
(ex. sports team rankings) |
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Scale of measurement: Interval
(properties) |
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Definition
Has identity, magnitude, and equal interval properties
Addition/subtraction are meaningful, but multiplication/division are not
(ex. Fahrenheit and Celsius scales) |
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Scale of measurement: ratio
(properties) |
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Definition
has identity, magnitude, equal interval and absolute zero properties
All arithmetic operations are meaningful
(ex. weight, #correct on a test) |
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| Advantages (2) of within-subj designs: |
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Definition
1. Allows the use of fewer subjects to obtain the same number of observations
2. Allows for greater statistical power than between subjects design
*because of these advantages, it is always best to use a WSD when possible |
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| problems (3) of within-subj designs: |
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Definition
practice effects (
carry over effects
sensitization effects |
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| what is a sensitization effect and how do we design to negate it? |
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Definition
occur when the subject realizes what the manipulations are in a study, and this awareness causes the subject to change behavior
negate by using between-subjects design |
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| what is a carry-over effect and how do we design to negate it? |
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Definition
occur when the effects of one treatment persist when another treatment is induced
negate by using between-subjects design |
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| Two methods of counter balancing: |
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Definition
1. Use all possible treatment orders (for 4 or fewer treatments)
2. Use a latin square (for 4 or more treatments) |
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| The extent to which your research provides a valid test of the relationship between the IV and the DV |
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| The probability of making a Type I Error given that our experiment found an effect of the IV on the DV |
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| The probability of making a Type II Error given that our experiment failed to find an effect of the IV on the DV |
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| statistical power (and why do we want it) |
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Definition
The probability that the experiment will find an effect of the IV on the DV if an effect exists
we want it because LOWERS the probability of type II errors |
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| factors (5) determining statistical power, and their relationships to that power |
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1. Alpha level (bigger = more power, but +p(type 1 error)
2. Effect size (bigger = +power)
3. Variability in the DV (lower = +power)
4. Sample size (bigger = +power)
5. Correlation between the IV levels (more[+ or -]) = +power) |
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| ways (3)to VALIDLY increase statistical power: |
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1. Choose IV levels that will maximize effect size (500mg and 501mg of a substance would be shit IV levels. Do 0mg and 500mg instead)
2.Try to lower the variability in the DV (make sure all conditions are as close to the same in the experiment across the population) (often lowers generalizability)
3. increase sample size (best choice) |
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| Finding an effect of the IV on the DV when in reality no such effect exists |
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| Failing to find an effect of the IV on the DV when in reality an effect DOES exist |
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| factors (2) that can increase your chances of type II errors: |
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Definition
1. Nuisance Variables (anything other than the IV which affects the DV. Not really a big problem)
2. Floor and ceiling effects (ex. super easy or super hard test) |
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Definition
| occurs when levels of the IV are so similar that their effects on the DV cannot be distinuished |
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| Can't prove null hypothesis |
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| it's impossible to PROVE the IV has no effect on DV. |
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| Factors producing type I errors: |
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Definition
1. Regression to the mean
2. Confounds |
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Definition
refers to the tendency for extreme values of a variable to fall closer to the group mean when retested
(a guy who ran his best time ever will almost certainly have a more average race next) |
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| control group (and why do we use them) |
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Definition
a group of subjects in a between subjects design that receives a treatment we know is ineffective at changing the DV
Makes sure effects are not due to regression to the mean |
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| types of counfounds (2) and solutions: |
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Definition
1) Counfounds due to subject assignment (nonrandom assignment) (probability of this =alpha)
2) Confounds due to manipulation of the IV (when IV has more effects than anticipated)~Surprise! Dynamite explodes, so juggling more sticks DOES increase depression |
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Term
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Definition
| anything other than the IV that can affect the DV |
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| floor and ceiling effects |
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| occur when the values of the DV are so low (floor) or so high (ceiling) that they are unlikely to be affected by the IV |
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Term
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Definition
anything that other than the IV that has an effect on the DV (nuisance variable) which varies NON-RANDOMLY with the IV
(IV goes up, this variable always goes up, or always goes down. et v v) |
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Term
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Definition
| an IV whose levels were chosen randomly from a population of possible values |
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| an IV whose levels were chosen NON RANDOMLY |
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| Aspects of a study that indicate to subjects how they are expected to respond |
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| A demand characteristic that occurs when subjects change their behavior due to UNINTENTIONAL CUES FROM THE RESEARCHER |
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| a demand characteristic that occurs when subjects change their behavior as a result of the EXPECTATION THAT CHANGE SHOULD OCCUR |
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| a demand characteristic that occurs when subjects change their behavior BECAUSE THEY KNOW THEY ARE BEING OBSERVED |
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| How to overcome placebo effects? |
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Definition
| Single blind or double blind study |
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| how to overcome rosenthal effects? |
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occurs when the DV is influenced by the IV ONLY BECAUSE the IV IS SOMETHING NEW
(christmas lights on taxi cars reducing collisions with the taxi, for example) |
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