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Statistical Methods In Psychology > Quiz 1 > Flashcards

Quiz 1 Flashcards

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1
Q

Two branches of statistical methods

A

Descriptive statistics & Inferential Statistics

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2
Q

Descriptive statistics

A

Psychologists use to summarize and describe a group of numbers from a research study.

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3
Q

Inferential statistics

A

Psychologists use to draw conclusions and make inferences that are based on the numbers from a research study but that go beyond the numbers.

  • Allows researcher to make inferences about large group based on smaller representative group
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4
Q

Variable

A

Condition or characteristic that can have different values

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5
Q

Value

A

Number or category

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6
Q

Score

A

A particular person’s value on a variable

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7
Q

Stress level, gender, and religion is an examples of

A

Variable

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8
Q

0,1,2,3,4; 25, 28; female; catholic is an example of

A

Value or score

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9
Q

Numeric variable/ Quantitative variable

A

Is a variable in which the numbers stand for approximately equal amounts of what is being measured

  • Continuous variable
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10
Q

Equal-interval variable

A

An equal-interval variable is measured on a ratio scale if it has an absolute zero point.

  • Numeric variable
  • Continuous variable
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11
Q

Rank-order variable/ ordinal variable

A

It is a variable in which the numbers stand only for relative ranking.

  • Numeric variable
  • Discrete variable
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12
Q

Nominal variable / Categorical variable

A

in which the values are names or
categories.

  • Discrete variables
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13
Q

Grade point average (GPA), scale stress level, and age is an example of

A
  • Numeric variable / Quantitative variable
  • Continuous variable
  • Equal-interval variable
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14
Q

The number of siblings a person has is measured on a ratio scale because a zero value means having no siblings. Time, weight, distance. Are examples of

A
  • Equal-interval variable
  • Continuous variable
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15
Q

Student’s class standing and position finished in a race. Are examples of

A
  • Discrete variables
  • Rank-order variable / ordinal variable
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16
Q

Gender and psychiatric diagnosis are examples of

A
  • Nominal variable / Categorical variable
  • Discrete variables
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17
Q

Discrete variables

A

Represent counts (e.g., the number of objects in a collection).

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18
Q

Continuous variables

A

Represent measurable amounts (e.g., water volume or weight).

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19
Q

Level of measurements

A

Nominal
Ordinal
Interval
Ratio

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20
Q

Nominal

A

The data can only be categorized

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21
Q

Ordinal

A

The data can be categorized and ranked

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22
Q

Interval

A

The data can be categorized, ranked, and evenly spaced

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23
Q

Ratio

A

The data can be categorized, ranked, evenly spaced, and has a natural zero.

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24
Q

City of birth, Gender, Ethnicity, Car brands, and Marital status are examples of

A

Nominal data

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25
Q

Top 5 Olympic medallists, Language ability (e.g., beginner, intermediate, fluent), Likert-type questions (e.g., very dissatisfied to very satisfied)

Are examples of

A

Ordinal data

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26
Q

Test scores (e.g., IQ or exams), Personality inventories, Temperature in Fahrenheit or Celsius

are examples of

A

Interval data

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27
Q

Height, Age, Weight, and Temperature in Kelvin are examples of

A

Ratio data

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28
Q

Mean

A

Sum of the scores divided by the number of scores

  • most common
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29
Q

Mode

A

Value with the most greatest frequency in a distribution

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30
Q

Median

A

Middle score when all the scores in a distribution is arranged from lowest to highest

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31
Q

When is mean used

A
  • With equal-interval variables
  • Very commonly used in psychology research
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32
Q

When is mode used

A
  • With nominal variables
  • Rarely used in psychology research
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33
Q

When is median used

A
  • With rank-ordered variables
  • When a distribution has one or more outliers
  • Rarely used in psychology research
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34
Q

mode = mean

A

a perfectly symmetrical unimodal distribution

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35
Q

Mode not equal mean

A

mode is not a good way of describing the central tendency of scores

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36
Q

Variability

A

the measure of how to spread out a set of scores is

  • the average of the squared deviations from the mean.
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37
Q

standard deviation

A

the square root of the average of the squared deviations from the mean

  • the most common descriptive statistic for variation
  • approximately the average amount that scores in a distribution varies from the mean.
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38
Q

Research’s definition of variance

A

the sum of squared deviation scores divided by 1 less than the number of scores

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39
Q

If the actual score is above mean

A
  • z-score
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40
Q

If the actual score is below mean

A

+ z-score

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41
Q

Z score

A

Number of standard deviations that a score is above (or below, if it is negative) the mean of its distribution

  • it is thus an ordinary score transformed to better describe the score’s location in a distribution.
42
Q

Raw score

A

Ordinary score (or any number in a distribution before it has been made into a Z score or otherwise transformed).

43
Q

Mean of any distribution of z scores =

A

= 0

44
Q

The standard deviation of any distribution of Z score =

A

= 1

45
Q

normal distribution

A

Frequency distribution that follows a normal curve.

46
Q

normal curve

A

Specific, mathematically defined, bell-shaped frequency distribution that is symmetrical and unimodal

  • distributions observed in nature and in research commonly approximate it.
47
Q

Percentage of scores between the mean and different standard deviations

A
  • 34% of score are always between the mean & 1 SD from the mean
  • 14% of scores are always between 1 & 2 standard deviations above mean
  • 50% if scores are below mean
48
Q

Sample

A

the part of the population about which you actually have information

  • In psychology research common to take samples to make inferences about the population
49
Q

Population

A

The entire set of things of interest.

50
Q

Random selection

A

Method for selecting a sample that uses truly random procedures (usually meaning that each person in the population has an equal chance of being selected)

  • Step 1: Researcher starts with complete list of population
  • Step 2: Randomly selects some of them to study
51
Q

Haphazard sampling

A

Taking whoever is available or happens to be first on a list

52
Q

Why Psychologists Study Samples Instead of Populations

A
  • Not practical
    - Point of the study is to make generalizations, not figure out answers based on population
  • Most common
    - Researchers study people who don’t differ greatly from the general population
53
Q

Methods of sampling

A

Haphazard selection - Taking whoever is available or happens to be first on a list

Random selection - Most ideal method of picking a sample to study

54
Q

Population parameter

A

actual value of the mean, standard deviation, and so on, for the population; usually population parameters are not known, though often they are estimated based on information in samples.

55
Q

Sample statistics

A

descriptive statistics, such as the mean or standard deviation, figured from the scores in a group of people studied.

56
Q

When population mean unknow =

A

best predictor is the sample mean

57
Q

When the population standard deviation, σ, is unknown

A

the sample standard deviation is used to estimate σ in the confidence interval formula.

58
Q

Confidence intervals

A

roughly speaking, the range of scores (that is,the scores between an upper and lower value) that is likely to include the true population mean
- more precisely, the range of possible population means from which it is not highly unlikely that you could have obtained your sample mean.

59
Q

Sample mean vary a lot =

A

can’t be confident est. is close to true pop. mean.

60
Q

Sample mean close to pop mean =

A

est. is pretty close

61
Q

Normally want to be ___ confidence about estimates

A

68%

62
Q

Psychologists use _____ confidence intervals

A

95% or 99%
(greater confidence = broader confidence interval )

63
Q

95% Confidence level =

A

confidence interval in which, roughly speaking,there is a 95% chance that the population mean falls within this interval.
- want area in normal curve on each side between mean & Z score that includes 47.5%
- Z score = -1.96 to 1.96

64
Q

99% Confidence level =

A

confidence interval in which, roughly speaking,there is a 99% chance that the population mean falls within this interval.

use Z scores for middle 99% of normal curve (49.5% above & below mean)

65
Q

Confidence limits

A

points at which a more extreme true population wouldn’t include sample mean 95% of time

66
Q

correlation

A

association between scores on two variables.

67
Q

Linear & Curvilinear Correlations

A

Linear = straight line
Curvilinear = not straight line

68
Q

No Correlation

A

No relationship between variables

69
Q

Positive correlation

A

relation between two variables in which high scores on one go with high scores on the other, mediums with mediums, and lows with lows

  • on a scatter diagram, the dots roughly follow a straight line sloping up and to the right.
70
Q

Negative

A

relation between two variables in which high scores on one go with low scores on the other, mediums with mediums, and lows with highs

  • on a scatter diagram, the dots roughly follow a straight line sloping down and to the right.
71
Q

Strength of the Correlation

A

how much there is a clear pattern of some particular relationship between two variables.

72
Q

“large” (or “strong”) linear correlation =

A

if the dots fall close to a straight line (.50)

73
Q

“small” (or “weak”) correlation =

A

if you can barely tell there is a correlation at all; the dots fall far from a straight line. (.10)

74
Q

“moderate” (also called a “medium” correlation) =

A

if the pattern of dots is somewhere between a small and a large correlation. (.30)

75
Q

How to measure what is a high score and what is a low score =

A

comparing scores on different variables in a consistent way (using Z score)

76
Q

(High Z score) x (High Z score) =

A

+ cross-product

Why?: scores above mean are + Z scores

77
Q

(Low Z score) x (Low Z score) =

A

+ cross-product

Why? Scores below mean are - Z scores neg. Times neg. Equal pos.

78
Q

(+ or -) of correlation coefficient =

A

direct of linear correlation between 2 variables

79
Q

Values of correlation coefficient - between 0 and 1 =

A

tell you strength of linear correlation

80
Q

Three Possible Directions of Causality

A

1) X could be causing Y.
2) Y could be causing X.
3) Some third factor could be causing both X and Y.

81
Q

Ruling Out Some Possible Directions of Causality

A

1) The future can’t cause the past
2) Strongest way to rule out possibilities = perform true experiment

82
Q

restriction in range

A

situation in which you figure a correlation but only a limited range of the possible values on one of the variables is included in the group studied.

83
Q

Attenuation

A

reduction in a correlation due to unreliability of measures

84
Q

The direction and strength of a correlation can be drastically distorted by

A

one or more individual’s scores on the two variables if each pair of scores is a very unusual combination. -Outliers-

85
Q

small correlations

A
  • have practical importance
  • impressive in demonstrating the importance of a relationship - if a study shows that the correlation holds even under what would seem to be unlikely conditions.
86
Q

Correlational results are usually presented in research articles either in the

A

1) Text with the value of r (and usually the significance level)
2) A unique table (a correlation matrix) shows the correlations among several variables.

87
Q

a correlation matrix

A

A unique table that shows the correlations among several variables.

88
Q

Predictor (X)

A

in prediction, variable that is used to predict scores of individuals on another variable.

89
Q

criterion (Y)

A

(usually Y ) in prediction, a variable that is predicted.

90
Q

Predictor (X) vs. criterion (Y) variables

A

With prediction, we have to decide which variable is being predicted from (Predictor X) & which variable is being predicted (Criterion Y)

91
Q

Linear prediction rule

A

The formula for making predictions; that is, a formula for predicting a person’s score on a criterion variable based on the person’s score on one or more predictor variables.

92
Q

Prediction in research articles

A

favored by research psychologists is a rule of the form:

“to predict a person’s score on Y, start with some baseline number, which we will call a, then add to it the result of multiplying a special predictor value, which we will call b, by the person’s score on X”

93
Q

What is a parametric statistic?

A

Standard deviation

94
Q

What statistics is/are unbiased estimators of their population parameters?

A

Mean

95
Q

SPSS only calculates ___ statistics

A

Inferential

96
Q

A pet owner found that on days her puppy napped a lot she had fewer headaches. This is an example of a ____ correlation

A

Negative

97
Q

A research calculated a -.23 correlation between variable X & variable Y. This is considered a ____ correlation

A

small

98
Q

Research calculated a -.23 correlation between variable X & variable Y. This is considered a ____ correlation

A

small

99
Q

A ____ confidence interval occurs between a mean plus/minus 1 z score

A

68%

100
Q

A formula for predicting score on a criterion variable based on a score on a predictor variable is called a(n)

A

linear prediction rule

Statistical Methods In Psychology (4 decks)

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