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psyc 209 exam 1
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Gravity
Terms in this set (68)
effect size
the magnitude of a relationship between two or more variables.
test statistic
the ration between systematic to unsystematic variance. (effect/error)
correlation coefficient
a single number, ranging from-1.0 to 1.0, used to indicate the strength and direction of an association
null hypothesis
in a common form of statistical hypothesis testing, the assumption that there is no difference, no relationship, or no effect in a population.
statistically significant
a conclusion that a result is extreme enough that it is unlikely to have happened by chance if the null hypothesis is true.
central tendency
a measure of what value the individual scores tend to center on.
standard deviation
square root of the variance
frequency distribution
a table that gives a visual picture of the observations on a particular variable. shows how many times each value occurred in the data set
frequency histogram
created on the data from a frequency distribution.
mode
the value of the most common score- the score that was received by more members of the group than any other. (bimodal= two modes, multimodal= more than two)
median
the value at the middlemost score of a distribution of scores- the score that divides a frequency distribution into halves.
mean
the average of all of the data scores
variance
to find: score- mean, deviation squared, sum all deviations and then divide by the degrees of freedom
Type 1 error
a "false positive" result from a statistical inference process, in which researchers conclude that there is an effect in a population when there really is none.
Type 2 error
a "miss" in the statistical inference process, in which researchers conclude that there is no effect in a population when there really is an effect.
categorical variables
a variable whose levels are categories (e.g., male/female)
quantitative variables
a variable whose values can be recorded as meaningful numbers.
reliability
the consistency of a measure
power
the probability that a study will show a statistically significant result when some effect is truly present in the population
manipulated variable
a variable in an experiment that researchers control by assigning participants to its different levels.
independent variable
a variable that is manipulated in an experiment. In a regression analysis, it is the variable used to explain variance in the criterion variable.
dependent variable
in an experiment, the variable that is measured, or the outcome variable. In a regression analysis, the single outcome, or criterion variable, that the researchers are most interested in understanding or predicting.
confounds
a potential alternative explanation for a research finding (a threat to internal validity).
Relationship between effect size, sample size, and significance
the stronger a correlation and the larger its effect size the more likely the correlation will be statistically significant. a small correlation will be statistically significant if it is identified in a very large sample.
between groups design
same as independent group design.. an experimental design in which different groups of participants are exposed to different levels of the independent variable such that each participant experiences only one level of the independent variable
within groups design
a study design in which each participant is presented with all levels of the independent variable
sum of squared error
how to find: x-the mean, squared, add all amounts.
cohen's d
the difference between two means divided by a standard deviation for the data
how to determine the % of the total variance that effect accounts for.
A pearson's r (ex:. 1) squared * 100. (1%)
meta-analysis
compiling of the data from multiple studies
skew
the symmetry of the distribution
positive skew
scores bunched at low values with the tail pointing to high values
negative skew
scores bunched at high values with the tail pointing to low values
kurtosis
the 'heaviness' of the tails
leptokurtic
positive kurtosis (heavy tails)
platykurtic
negative kurtosis (light tails)
3 rules for establishing causation
1. cause and effect must occur close together in time.
2. the cause must precede an effect
3. the effect should never occur w/out the presence of the cause.
five steps to the research process
1. initial observation
2. generating theories
3. generating hypotheses
4. collect data to test theory
5. analyze data
population
the collection of units to which we want to generalize a set of findings or a statistical model.
sample
a smaller collection of units from a population used to determine truths about the population
mean as a model
outcome= model + error
deviation
the difference between the mean and an actual data point. Can be calculated by taking each score and subtracting the mean from it
confidence intervals
range within which the population mean will fall in a certain percentage of samples. Statistically significant: no overlap. Not statistically significant: overlap.
R
the strength of relationship between two variables
numeric variables
the variable that contains numbers and is the default. In the variable view.
string variables
consist of strings of letters. such as: names. In the variable view.
scale
values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars
nominal
A variable can be treated as nominal when its values represent categories with no intrinsic ranking; when two things that are equivalent in some sense are given the same name (or number), but there are more than two possibilities.
ordinal
categories are ordered.
data view
used for entering data
variable view
used for defining characteristics of the variables within the data editor.
Data view: rows
data from different things (measured more than once) go in different rows. Ex: data from each person is represented in different rows.
Variable view: rows
every row represents a variable.
Variable view: columns
any variable that defines different groups of things (such as when a between-groups design is used and different participants are assigned to different levels of the independent variable). is represented in one column. any variable measured with the same participants (a repeated measure) should be represented by several columns (each column represents a different level of the repeated-measures variable).
Data view: columns
data from the same things go in different columns. Ex: when measuring an outcome, each prod will be represented by a column.
coding variable
changing string variables to numeric variables. Ex: using "0" for male and "1" for female.
syntax
a record of what you have done
How to code variables
1. go to variable view
2. click on value labels
How to find the mean on SPSS
1. highlight data
2. click analyze
3. click descriptive statistics
4. click correct column name
5. go to options: choose mean
how to determine what our codes meant
1. go to variable view
2. click values
null hypothesis testing
as a test statistic increases, it becomes less likely that the null hypothesis is true
law of large numbers
the larger the sample size, the more likely it is that the sample mean is close to the population mean (as long as the sample is representative)
Range
smallest score subtracted from the largest
large standard deviation
not a good model.. far away from the mean
small standard deviation
good model.. close to the mean.. points are close together
standard error
how representative a sample is likely to be of the population. a small standard error better reflects the population
STandard error
a measure of dispersion within a population
STandard deviation
a measure of dispersion within a sample
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