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psy 201 exam 1
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Flashcards
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Terms in this set (56)
variable
a feature that varies
constant
a characteristic or condition that does not change
but is the same for all individuals
datum
a single measure
data
more than one measure
data set
a whole set
What is the difference between descriptive and inferential statistics?
Descriptive statistics describes sets of data. Inferential statistics draws conclusions about the sets of data based on sampling.
What are the four scales of measurement?
nominal, ordinal, interval, ratio
nominal scale
naming/classifying | measurement in which numbers are assigned to objects or classes of objects solely for the purpose of identification
ordinal scale
a scale of measurement in which the measurement categories form a rank order along a continuum
ratio scale
has an absolute zero | # of airplanes crashes
interval scale
has no true zero | example is temperature
discrete variable
Variables that consist of separate indivisible categories, usually whole numbers
continuous variable
infinite number of possible values that fall between any two observed values
experimental design
Manipulate a variable,
Control for all confounding variables,
Test cause-effect relationships
quasi-experimental design
Manipulate a variable
Unable to control for all confounding variables
Unable to test cause-effect relationships
correlation
No manipulation
Testing relationships between two measured variables
No independent variable
population
All individuals (or items) in the group of interest
sample
A set of individuals (or items) selected to represent the population
population parameter
The value of interest from the population
(e.g., proportion who view the two pink cards as different)
sample statistic
The value collected from the sample
hypothetical constructs
Depression
Anxiety
Aggression
Assertiveness
Optimism
Love
Dependence/Independence
Maturity
PersonalityTraits
independent variable
variable that is manipulated
dependent variable
The measurable effect, outcome, or response in which the research is interested.
dependent on other variables. It is the variable that is measured or tested by a researcher.
notation
Numbers and how we symbolize
them
raw score
rawscore = X or Y
each score gets a number X1 X2 X3
number of scores in a set
N (if the whole set)
n (if a sample of a larger set)
sum
Sum = E
The frequency column includes
The value of the measure
The f(x) column reflects
The frequency of scores times the score value for that row
To calculate proportion of scores at a value, we find
The frequency of scores at that value divided by the total number of scores
frequency histogram
Used to plot the frequency distribution
- When the numbers or categories are on an interval or ratio scale, use a "histogram"
• Bars touch to note that the dependent variable is a scale rather than nominal categories
bar chart
Use when the X-axis Variable is from a Nominal or Ordinal Scale
shapes of distributions and skewness
uni modal, bio modal, positive and negative
central tendency
Many scores clustered in the center
Few extreme scores
variability
Scores distributed across full range
More extreme scores
Histograms and polygons should be used for measures from
A ratio or interval scale of measurement
Bar graphs should be used for measures from
A nominal or ordinal scale of measurement
Which of the following is true for a frequency histogram?
The Y-axis should start at 0
The Y axis should be 2/3 the length of the X axis
Either the height of the bar or value of the dot should be placed at the level of the frequency of scores at that value.
Rules for Grouped Frequency Distributions
1) approximately 10 intervals
2) width of interval is simple
(e.g., whole number, or by 5's or 10's)
i.e., avoid unusual widths such as 3 or 4.5
3) Bottom score should be a multiple of the interval width. (if interval is 5, bottom score should be 0, 5, 15, 20 ... , not 3, 8 ...)
4) All intervals must be same width
for the type of scale
rank or percentile rank:
% of scores that fall at or below your score
For example, 6 has a percentile
rank of 81
percentile:
Score associated with the percentile rank.
For example, the 81st percentile is 6.
Finding percentile ranks for discrete variables
Steps:
1) Construct a frequency distribution
2) Then use a cumulative frequency Column (f x x)
3) Look at the cumulative
frequency for the score of 3
3) Percentile rank = (cf/n) * 100
( 14 / 20 )
100 = 0.7
100 = 70%
70% of scores were 3 or below
3 is the 70th percentile
**this is true if you are using a discrete variable
Percentile X f cf rank
How to find percentile rank when you have a continuous variable?
use interpolation
When using grouped frequency distributions, the percentile RANK for an interval is always associated with
upper limit
goal of central tendency
describe a group of scores with a single measure which will be representative of all the scores.
- Make a large amount of data digestible- ideally we choose a value in the middle of the distribution
3 central tendencies
mean, median, mode
mean
the arithmetic average of a distribution, obtained by adding the scores and then dividing by the number of scores
Balance point for the distribution
median
the middle score in a distribution; half the scores are above it and half are below it
mode
the most frequently occurring score(s) in a distribution
Characteristics of the mean
Changing any score o radding/subtracting a score will change the mean
- if either the sum of X or the N change, the mean will change
If you Add or subtract a constant to/from each score, then add/subtract it from the mean to get the correct value
If each score is multiplied/divided by a constant, then do the same to the mean
One problem with the mean?
It is heavily influenced by extreme scores
Why use the median?
the median is not influenced by extreme scores. It is more accurate for reporting some measures (e.g., salaries)
Use the Median when...
There are extreme scores
- Scores with undetermined values • e.g., one subject did not finish
- Open-ended distribution • 1,2,3,4,5ormore
- Can't use mean without exact numbers for those over 5.
- data come from an ordinal scale
Use the mode when...
you have a nominal scale of measurement
- Advantages of mode• easy to compute• used with any scale of measurement
- provides a sensible measure when the scale of measurement is discrete
• the mode will always reflect possible units (e.g, 2 children per household vs. mean of 2.4 children per house).
In symmetrical distributions...
the median will be in the exact center
the mean will be in the exact center
the mode will be in the exact center in a unimodal distribution
Means are often represented visually with?
a bar graph
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