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Hindsight bias
thinking you knew something all along after outcome
Confirmation bias
Tendency to pay more attention to info that supports our preexisting ideas
Quantitive
Numerical
Hypothesis
relation between two variables, must be falsifiable
Operational definitions
explain how you will measure hypotheses
Representative sampling
goal of selecting sample that represents the larger population
Random sampling
actually random sampling, done by computer
Convenience sampling
collecting data form group that’s easily accessible to you
Stratified sampling
allows researcher to ensure that sample represents pop according to proportion
Confounding variables
any other possible thing that may affect outcome
Random assignment
each participant has equal chance of being placed into either group
Experimenter bias
Unconsciously treating people in each group differently to increase chance of confirming one hypothesis
Double blind study
Participants and researcher can’t affect outcome
Single blind study
participants don’t know which group they’ve been assigned to
Social desirability bias
tendency to try and give answers that reflect well upon oneself
Positive correlation
Presence of one variable predicts presence of other
Negative correlation
Presence of one thing predicts absence of other
Ex post facto study
Seeking to control all other aspects of the research process
Likert scales
Used on surveys for level of agreement/ disagreement
Directionality problem
inability to tell which variable came first
third variable
confounding variable
Naturalistic observation
observing participants in their natural habitat without interacting
central tendency
attempt to mark canter of a distribution, mean, median, mode
Bimodel
2 score appear equally frequently, 2 modes
Frequency polygons
line graphs
Histograms
bar graph
positively skewed
outlier of extreme score, mean higher than median
negatively skewed
outlier that skews it lower, mean lower than median
measures of variability
attempt to depict diversity of distribution (range, standard deviation)
What do standard deviation and variance measure
average distance of any score from the mean, the higher, the more spread out the distribution
Standard deviation is
square root of variance
central tendency
attempt to mark center of a distribution
central tendency examples
mean, median, mode
z scores
measure distance of a score from the mean in units of standard deviation
negative z score means
scores below the mean
normal curve
theoretical bell-shaped curve for which area under curve lying between 2 z scores have been predetermined
what does normal curve allow
allows you to calculate the approx percentage of scores falling between any given z scores, bell shaped
percentiles
distance of the score from 0
z scores
distance of score from mean
percentiles vs z scores
if you score 50th percentile, you are better than 50% of people., but z score of 0
correlations
measure relationship between z variables
positive correlation ex
two variables move in same direction, studying and getting good grades
negative correlation ex
two variables move in opposite directions, cutting classes and bad grades
correlation coefficient
strength of a correlation
-1 correlation coefficient
perfect negative correlation
+1 correlation coefficient
perfect positive coefficient
0 correlation coefficient
weakest possible correlation, none
scatterplot: graphing correlations. Regression line slopes up=
positive correlation
Inferential statistics
determine whether findings can be applied to the larger population from a sample
p value
probability that diff between groups is due to chance
smaller p value =
more significant results
statistically significant p value cutoff
0.05
effect size
quantifying practical significance, large sample is less likely for stastically significant finding
replication crisis
replicating a study using same method to try and get same results
meta analysis
combining results of many studies on same topic to approximate an average
APA
american psychological association