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100 Cards in this Set

  • Front
  • Back
nominal categories
yes/no
apples oranges
not numeral
ordinal
1st 2nd 3rd
oredered levels
interval
intervals between levels are equal
no abs 0
ratio
has a 0
three ways of describing results
1. comparing group percentage
2, correlating individual scores
3.comparing group means interval and ration data
comparing group percentage
used when data is nominal
(comparing males and females .. do you like to travel?)
correlating (co-relating)
individual scores- r of individual on 2 distinct score
(where one sits in class and their grade... ranges from 1 to -1
graphing frequency distribution
pie. bar, freq polygons, histograms
pie
for nominal data shown in the percent form
bar graph
use separate and distinct
histograms
values are continuos ie age blood pressure
frequency polygons
ratio interval.. freq distribution is shown with a line
mean
score that divides the group in half
mode
most frequent score
variability
the amount of spread in the distribution of scores
standard deviation
(s) indicates the avg deviation from the mean, small where scores are clustered around the mean
variance
(s^2) square root of standard deviation
correlation coefficients
describe the strength of relationships
strength of relationship
abs value of r
pearson r sorrelation coefficient
need two data points from each P, interval/ ratio data
direction of relationship
+ if one variable increases and the other does too
- if one increases and the other doesnt
which is has the stronger relationship??
-.66 or +.56
-.66
restriction of range
need to get as wide a range as possible for each variable to determine the magnitude/ strength of r. If Ps are too homogenous then you cannot see the true strength
curvilinear relationship
cannot be seen with r it is designed to look only at linear relationships btw two variables
effect size
strength of association btw variables
indicator of effect size
pearson r coefficient
advantage of effect size
consistent across all types of studies
effect size ranges..
0 to 1 and is generally reported as small med and large
transforming effect size to a percentage (percent of shared variance btw the two variables)
square value
ex. r=.5 becomes .25 which means between the two variables 25% is shared leaving 75% still unaccounted for
statical significance
one wants to know if the results will hold up if the experiment is repeated several times (inferential statistics)
inferential statistics
infers weather the results will "hold up"... makes researchers focus on internal validity
regression equations
calculations used to predict a persons score on one variablr when that persons score on another variable is already known

ex college uses act
regression equation form
y= a +bX
y=
score you wanna predict
x=
score that is known
a=
constant
multiple correlation
combines a number of predictor variables to increase the accuracy of prediction
multiple correlation equation
y=a+b(sub one) x (sub one) + b (sub 2) X (sub two) ........
each variable doesnt equally predict in ...
multiple correlation equation
partial correlation and 3rd varable problem (often seen in no experimental research)
provides a way of statically controlling extraneous third variables
structural models
allow researcher to test how obtained data fit in a theoretical structural model
structural models
expected pattern of relationships among a set of variables
path analysis
Inferential statistics are necessary because..
the results of a given study are based on a single participant pool (sample) and the researcher really wants to know if the results would be true for the whole population
inferential statistics
Allows researchers to make inferences about the true difference in the population on the basis of the sample data.
inferential statistics
Gives the probability that the difference between means reflects random error rather than a real difference
null hypothesis
pop means are equal, the observed diff is due to random Error NOT THE IV
research hypothesis
THERE WAS AN EFFECT OF THE IV
statical significance
how strongly we can infer that that the research hypothesis is correct..( by doing this we reject the null hypotheses)
you can never PROVE the hypothesis but you can...
REJECT the null hypothesis
statical significance
high sta sig means a very low chance that the difference or results are due to chance or error
probability
the likelihood of the there being a diff in the sample means when there is no diff in the pop means.. so if random error is operating at 1% then that means a 99% chance
sample size
as you increase sample size random error influence drops
increase in sample size
so as your sample size increase you are more confident that the outcome you find in your experiment is actually diff than the null hypothesis (the means are equal in the pop) and you can reject the null hypothesis and accept the research hypothesis. you cannot say you have proven the research hypothesis
t test is used to..
reject the null hypothesis
the t test is used with experiments with
2 mean (simple experiments)
with the t test you look at the ..
mean difference between groups.. the null hyp expects the to be 0
the value of t increases as..
the difference between the sample means increase
t=
group difference/
within group variability

t is a ratio of two aspects of the data, the difference between the group means and the variability within groups
within group variability is measured by
variance (square root of Standard deviation) which tell us how far scores deviate from the mean
within group variance becomes lower when..
you increase sample size
you want within group error to be small so..
t can be bigger
you can make t bigger by...
having a larger sample size
you need to know two things to know if the t test is significant
deggree of freedom and
one-tailed vs. two tailed
degree of freedom
(df) total number of participants minus the number of groups (ex with 2 groups each with 10 par, its 10+10-2=18)
degree of freedom is useed to..
get the critical value by selecting it from a table by using the degree of freedom
also in the table you must choose either one tailed or two tailed to get the critical value
one tailed two tailed
the f test is used when
used when > 2 levels of one of two types of variance: systementic and error.
systematic variance
deviations of the group means from the grand mean ( which is the mean score of all individuals in ALL groups)
error variance
the deviations of individual individuals scores in each group from their respective group means (“within group variance”)
statical significance( alpha)
chosen significance level (alpha level) indicates how confident you wish to be when making the decision if your sample results are reliable , and do reflect the population. in psychology we tend to use .05 .01 or .001 alpha keveks (95%, 99% 99.9 %)
knowing t or f are useful for
knowing the effect size of a correlation coefficent
type I error
Made when the null hypothesis is rejected but the null hypothesis is actually true... means saying there is a diff due to the IV when there isnt
type II error
the null hypothesis is accepted when the research hypothesis is true.. means saying there is not a diff due to the IV when there really is
Researchers traditionally have used...
either a .05 or a .01 significance level in the decision to reject the null hypothesis(alpha = .05 or .01)
Researchers generally believe that the consequences of making a Type _____ error are more serious
I,
for jurors, incarcerating an innocent person is worse (I) than letting a guilty person walk (II)
power analysis
statical test that determines the optimal sample size based on probability of correctly rejecting the null hypo. in other words what is the optimal number if subjects need in the experiment considering the effect size and alpha level
Smaller effect sizes require....
larger samples to be significant
Researchers usually use a power between...
.70 and .90
Scientists attach ____ importance to results of a single study
little
scientists look at results of
replicated studies
reporting the significance of the pearson r correlation coefficient
is the ‘co relationship” correlation between the two variables stastically significant
Software Programs
SPSS
SAS
Minitab
Microsoft Excel
steps in analysis
Input data
Conduct analysis
Interpret output
One Independent Variable– Two Groups Only (control and experiment)
Nominal Scale Data
ordinal
interval or ratio
Nominal Scale Data- chi square test (
Ordinal Scale Data- mann-whitney U test
Interval or Ratio Scale Data- t-test
One Independent Variable– Three or More Groups Ordinal Scale Data
nominal
ordinal
interval
Nominal Scale Data- chi square test
Ordinal Scale Data- kruskal- wallace H test
Interval or Ratio Scale Data- ANOVA
Two or More Independent Variables
nominal
ordinal
interval or ratio
Nominal Scale Data - chi-square
Ordinal Scale Data – no appropriate test is available
Interval or Ratio Scale Data – two-way analysis of variance (ANOVA)
generalization to other participants of research participants
college students can be too restrictive to many studies
volunteers can have prior knowledge and seek approval more
gender
cultural
...ok to use theses groups if the same result would be found in a different population
cultural consideration
today college students are more culturally diverse
important to be aware that the ways in which the operational definition of the construct are grounded in..
cultural considerations
experimenter's influence need to be kept..
constant throughout the experiment (equal across groups)
ex. personality, gender, experience
participants do better when tested by the opposite gender
pretests and generalization
not like the real world
but allows researcher to assess mortality effect
use soloman 4 group design
mundane realism
weather the experiment bears similarity to events that occur in the real world refers to the superficial physical characteristics of the situation and usually has little external validity (bo bo doll)
experimental realism
wether the experiment has impact on the participants, involves them psychologically and makes them take the experiment seriously and makes them behave normally while under similar constructs (standford study... prison)
mutual benefits of lab and field findings
are complimentary and allow greater mundane and experimental realism and greater control of the confounding variables
importance of replications:
exact replication
an attempt to replicate precisely the procedures of a study to see wether the same results are obtained
conceptual replications
the use of differential procedures to replicate a research finding
(just has the same concept)
literature reviews :
summarize , tell what findings are strongly supported and which ones are weakly supported, points where research is inconsistent or lacking, discuss future directions for research
meta analysis
method for determining the reliability of a finding by examining the results from many different studies

researcher combines results from diff studies using diff measures of a construct

important in allowing effect size to be measured when having the effect size of many studies
goal of psychology
to promote human welfare
impact of psychology
health
law and criminal justice
education
work environments