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psych stats Flashcards

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1
Q

2 BRANCHES OF STATISTICS

A

DESCRIPTIVE STATISTICS
and
INFERENTIAL STATISTICS

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2
Q

Use sample data to make general estimates about the larger population
infer, or make an intelligent guess about the population

A

inferential statistics

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3
Q

Organize, summarize, and communicate a group of numerical observations
Describes large number of data in a single number or in just as few numbers

A

descriptive statistics

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4
Q

A set of observations drawn
from the population if interest.

A

sample

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5
Q

Includes all possible
observations about which we’d
like to know something

A

population

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6
Q

finding something that needs explaining
“ Why are there many problematic personalities in reality shows like Big Brother?

A

Initial observation

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7
Q

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

A

Generating theories and testing them

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8
Q

3 types of variables

A

independent, dependent, confounding

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8
Q

When we collect data we need to decide on two things:
(1) what to measure
(2) how to measure it.
Hypothesis Testing

A

Data Collection to Test theory

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9
Q

has at least two levels that we either manipulate
or observe to determine its effects on the dependent variable

A

independent variable

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10
Q

is the outcome variable that we hypothesize to be
related to, or caused by, changes in the independent variable.

A

dependent variable

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11
Q

is any variable that systematically varies with the
independent variable so that we cannot logically determine which variable
is at work.

A

A confounding variable

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12
Q

In experimental work the cause, or independent variable, is a

A

predictor

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13
Q

the effect, or dependent variable, is simply

A

an outcome

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14
Q

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

A

discrete observations

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15
Q

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

A

variables

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16
Q

2 types of discrete observation

A

nominal and ordinal variable

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17
Q

2 types of continuous observation

A

interval and ratio variables

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18
Q

Use for observation that have categories or
names as their values

A

nominal variables

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19
Q

Are observations that have rankings (i.e. 1st, 2nd , 3rd,
…) as their values

A

ordinal variables

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20
Q

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

A

CONTINUOUS VARIABLES

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21
Q

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

A

INTERVAL VARIABLES

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22
Q

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

A

measurement error

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23
Q

Are variables that meet the criteria for interval variables but also have meaningful
zero points.
 Examples: weight, height

A

Ratio variables

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24
Q

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.

A

measurement error

25
Q

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

A

reliability and validity

26
Q

refers to whether an instrument measures what it was designed to
measure

A

validity

27
Q

refers to the consistency of a measure

A

reliability

28
Q

necessary but not sufficient condition of a measure.

A

validity

29
Q

is the process of drawing
conclusions about whether a particular relation between variables is supported by
the evidence

A

hypothesis testing

29
Q

Social scientists use research to test their ideas through a specific statistics-based
process called

A

hypothesis testing

30
Q

specifies the operations or procedures used to measure or
manipulate a variable.
 Example: How do we operationalize anxiety?

A

operational definition

31
Q

Data Collection Research Methods

A

Correlational Research Methods
and
Experimental Research Methods

32
Q

also called as cross-sectional research

A

correlational research

33
Q

(where we manipulate one variable to see its effect on another) is that
experimentation involves the direct manipulation of variables.

A

experimental research

33
Q

(where we observe what naturally goes on in
the world without directly interfering with it

A

correlational research

34
Q

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.

A

random assignment

35
Q

participants
experience one, and only one, level of the independent
variable.

A

between-groups research design

36
Q

variable that is manipulated

A

independent variable

36
Q

the different levels of
the independent variable are experienced by all
participants in the study; also called a repeated-measures
design.

A

within-groups research design

37
Q

variable that is being measured

A

dependent variable

37
Q

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.

A

Between-Groups Design:

37
Q

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.

A

Within-Groups Design

37
Q

The basic ingredients of a data set are called the

A

Raw scores

38
Q

in a within-groups research design, the different levels of
the independent variable are experienced by all
participants in the study also called

A

repeated measures design

39
Q

data that have not yet been transformed or analyzed

A

raw scores

40
Q

which describes the pattern of a set of numbers by displaying a count or proportion for each possible value of a variable

A

frequency distribution

41
Q

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.

A

frequency table

41
Q

Determine the highest score and the lowest score.

A

1st step in creating frequency table

42
Q

Create two columns: the first is labeled with the variable name, and the second is labeled “frequency.”

A

2nd step creating frequency table

43
Q

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.

A

3rd step creating frequency table

44
Q

Count the number of scores at each value, and write those numbers in the frequency column

A

4th step in creating frequency table

45
Q

measures the fraction of the total group that is associated with each score.

A

proportion

46
Q

measures that describe the distribution of scores and can be incorporated into the table.

A

proportion and percentage

47
Q

Because proportions describe the frequency (f) in relation to the total number (N), they often are called

A

relative frequencies

48
Q

associated with each score, you first find the proportion (p) and then multiply by100:

A

percentage

49
Q

is a visual depiction of data that reports the frequencies within a given interval rather than the frequencies for a specific value.

A

a grouped frequency table

50
Q

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

A

Inferential statistics

51
Q

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.

A

descriptive statistics

52
Q

the ability of the measure to produce
the same results under the same conditions.
To be valid must be ___

A

Reliability

psychology statistics (1 decks)

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