psych stats Flashcards
2 BRANCHES OF STATISTICS
DESCRIPTIVE STATISTICS
and
INFERENTIAL STATISTICS
Use sample data to make general estimates about the larger population
infer, or make an intelligent guess about the population
inferential statistics
Organize, summarize, and communicate a group of numerical observations
Describes large number of data in a single number or in just as few numbers
descriptive statistics
A set of observations drawn
from the population if interest.
sample
Includes all possible
observations about which we’d
like to know something
population
finding something that needs explaining
“ Why are there many problematic personalities in reality shows like Big Brother?
Initial observation
Theory: (1)People with narcissistic personality disorder are more likely to audition
for Big Brother than those without.
(2) Producers will more likely select people with narcissistic personality to
be contestants than those with less extreme personalities
hypothesis testing
Generating theories and testing them
3 types of variables
independent, dependent, confounding
When we collect data we need to decide on two things:
(1) what to measure
(2) how to measure it.
Hypothesis Testing
Data Collection to Test theory
has at least two levels that we either manipulate
or observe to determine its effects on the dependent variable
independent variable
is the outcome variable that we hypothesize to be
related to, or caused by, changes in the independent variable.
dependent variable
is any variable that systematically varies with the
independent variable so that we cannot logically determine which variable
is at work.
A confounding variable
In experimental work the cause, or independent variable, is a
predictor
the effect, or dependent variable, is simply
an outcome
Can take only specific values; no other
values can exist between these numbers
Example:
If we measure the number of times a study
participants gets up early in a particular week,
the only possible values would be whole
numbers. It is reasonable to assume that each
participant could get up early 0 to 7 times in
any given week, but not 1.6 or 5.92 times
discrete observations
Observations of physical, attitudinal, and behavioral characteristics that can on
different values.
Behavioral scientists often study abstract variables such as motivation, self-
esteem and attitudes
variables
2 types of discrete observation
nominal and ordinal variable
2 types of continuous observation
interval and ratio variables
Use for observation that have categories or
names as their values
nominal variables
Are observations that have rankings (i.e. 1st, 2nd , 3rd,
…) as their values
ordinal variables
Can take a full range of values; an infinite number of
potential values exists.
Example:
One person might complete a task in 12.389 seconds.
Someone else might complete it in 14.740 seconds. Limited only by the number of
decimal places we choose to use
CONTINUOUS VARIABLES
Are used for observations that have numbers as their values; the distance between pairs of consecutive number is assume to be equal.
Examples: temperature, IQ, SAT/ACT test scores
INTERVAL VARIABLES
There will often be a discrepancy between the
numbers we use to represent the thing we’re
measuring and the actual value of the thing
we’re measuring (i.e. the value we would get if
we could measure it directly). This discrepancy
is known as
measurement error
Are variables that meet the criteria for interval variables but also have meaningful
zero points.
Examples: weight, height
Ratio variables
What do you call this example?
Imagine that you know as an
absolute truth that you weight 83 kg. One day
you step on the bathroom scales and it says 80
kg. There is a difference of 3 kg between your
actual weight and the weight given by your
measurement tool (the scales): there is a
measurement error of 3 kg
Although properly calibrated bathroom scales
should produce only very small measurement
errors (despite what we might want to believe
when it says we have gained 3 kg), self-report
measures do produce measurement error
because factors other than the one you’re trying
to measure will influence how people respond to
our measures.
measurement error
One way to try to ensure that measurement error is kept to a minimum is
to determine properties of the measure that give us confidence that it is
doing its job properly
reliability and validity
refers to whether an instrument measures what it was designed to
measure
validity
refers to the consistency of a measure
reliability
necessary but not sufficient condition of a measure.
validity
is the process of drawing
conclusions about whether a particular relation between variables is supported by
the evidence
hypothesis testing
Social scientists use research to test their ideas through a specific statistics-based
process called
hypothesis testing
specifies the operations or procedures used to measure or
manipulate a variable.
Example: How do we operationalize anxiety?
operational definition
Data Collection Research Methods
Correlational Research Methods
and
Experimental Research Methods
also called as cross-sectional research
correlational research
(where we manipulate one variable to see its effect on another) is that
experimentation involves the direct manipulation of variables.
experimental research
(where we observe what naturally goes on in
the world without directly interfering with it
correlational research
is a process used in experiments to assign participants to different groups (e.g., treatment and control groups) in a way that each participant has an equal chance of being placed in any group. This helps ensure that the groups are similar in all respects before the treatment or intervention is applied.
random assignment
participants
experience one, and only one, level of the independent
variable.
between-groups research design
variable that is manipulated
independent variable
the different levels of
the independent variable are experienced by all
participants in the study; also called a repeated-measures
design.
within-groups research design
variable that is being measured
dependent variable
The company randomly assigns half of the employees to Training Program A and the other half to Training Program B. They compare performance after the training.
Between-Groups Design:
All employees complete both Training Program A and Training Program B, but in a randomized order. Each employee’s performance is compared after each training session to see which program is more effective.
Within-Groups Design
The basic ingredients of a data set are called the
Raw scores
in a within-groups research design, the different levels of
the independent variable are experienced by all
participants in the study also called
repeated measures design
data that have not yet been transformed or analyzed
raw scores
which describes the pattern of a set of numbers by displaying a count or proportion for each possible value of a variable
frequency distribution
is a visual depiction of data that shows how often each value occurred, that is, how many scores were at each value. Once organized, the data can be displayed as a grouped frequency table, a frequency histogram, or a frequency polygon. These four methods of visually organizing data represent the basic tools in a statistician’s toolbox.
frequency table
Determine the highest score and the lowest score.
1st step in creating frequency table
Create two columns: the first is labeled with the variable name, and the second is labeled “frequency.”
2nd step creating frequency table
List the full range of values that encompasses all the scores in the data set from highest to lowest. Include all values in the range, even those for which the frequency is 0.
3rd step creating frequency table
Count the number of scores at each value, and write those numbers in the frequency column
4th step in creating frequency table
measures the fraction of the total group that is associated with each score.
proportion
measures that describe the distribution of scores and can be incorporated into the table.
proportion and percentage
Because proportions describe the frequency (f) in relation to the total number (N), they often are called
relative frequencies
associated with each score, you first find the proportion (p) and then multiply by100:
percentage
is a visual depiction of data that reports the frequencies within a given interval rather than the frequencies for a specific value.
a grouped frequency table
A fitness researcher is investigating the relationship between exercise habits and health outcomes. They collect data from 500 participants on how many hours per week they exercise and their blood pressure levels.
The manager calculates the average customer satisfaction score from a scale of 1 to 10 based on the survey results.
The manager also groups the satisfaction scores to see how many customers rated their experience between 1-3, 4-6, and 7-10.
Based on this sample of 200 customers, the manager estimates that 80% of all future customers will likely rate their satisfaction above 7.
The manager performs a test to see if there’s a significant difference in customer satisfaction between lunch and dinner service times
Inferential statistics
A restaurant manager collects feedback from 200 customers about their dining experience over the past month. The manager wants to both summarize the feedback and make decisions about possible improvements.
The researcher calculates the median number of exercise hours per week for all participants.
The researcher estimates that individuals who exercise more than 5 hours a week are likely to have lower blood pressure in the general population.
The researcher creates a graph showing the distribution of exercise hours among participants, indicating how many participants exercise 1-3 hours, 4-6 hours, etc.
The researcher performs a statistical test to determine whether people who exercise at least 5 hours a week have significantly lower blood pressure than those who exercise less.
descriptive statistics
the ability of the measure to produce
the same results under the same conditions.
To be valid must be ___
Reliability
