Psyc2010 Chapters 1 To 5

Front 1

What is statistics?

Back 1

-the practice of analyzing numerical data

-a set of math procedures to organize, summarize, and interpret

-telling a story with data

Front 2

Why is statistics important?

Back 2

-doing and understanding research

-criticize

-replicate

Front 3

What is a population?

Back 3

-the set of all the individuals of interest in a particular study (ie Canadians, women, preteens etc)

Front 4

Does a population have to be made up of people?

Back 4

no, can be rats, corporations, parts made in a factory, etc

Front 5

What is a sample?

Back 5

a set of individuals selected from a population , usually to represent the population in a research study

Front 6

What is the relationship between a sample and a population?

Back 6

-the sample is selected from the population, sample participates in study, results are generalized to the population

Front 7

What two general categories can statistical procedures be classified into?

Back 7

-descriptive and inferential

Front 8

what are descriptive statistics?

Back 8

-procedures that summarize, organize, and simplify data

-results in tables, graphs, or single numbers that consolidate a large amount of information (ie average)

Front 9

What is the difference between a parameter and a statistic?

Back 9

-a parameter is a characteristic that describes a POPULATION (ie population average)
-a statistic is a characteristic that describes a SAMPLE

Front 10

What are inferential statistics?

Back 10

-techniques that use sample data to make generalizations about populations

Front 11

we use sample statistics to infer what the ______________________ is likely to be.

Back 11

population parameter

Front 12

What is sampling error?

Back 12

-the discrepancy (difference, amount of error) between a sample statistic and its population parameter

-sample statistics vary from one sample to another and typically are different from the corresponding population parameters

Front 13

What is a common example of sampling error?

Back 13

-sample proportion (ie voting, female/male ratio etc)

Front 14

What is sampling error caused by?

Back 14

-chance
-sampling bias

Front 15

What are the steps to using statistics in research?

Back 15

1: Experiment (ie Group A coffee, Group B water)
2: Descriptive Statistics (results: A=4hrs, B=2hrs)
3: Inferential Statistics (sample data show a 2 hr difference)

Front 16

we use ___________ statistics to decide whether results are a real difference, or due to sampling error

Back 16

-inferential

Front 17

What is a variable?

Back 17

a characteristic or condition that changes or has different values for different individuals

Front 18

What are the two main kinds of variables?

Back 18

-characteristics that differ from person to person
-environmental conditions that change

Front 19

What is a data set?

Back 19

the complete set of data/scores

Front 20

What is a datum? More commonly called?

Back 20

-the measurement or observation obtained for each individual

-AKA a score or raw score

Front 21

Most research is intended to examine the ____________________.

Back 21

relationship between variables

Front 22

What is a constant? AKA

Back 22

-a characteristic or condition that does not change across individuals
-AKA controlled variable

Front 23

What to quantitative and qualitative variables measure differences in?

Back 23

quantitative = amount

qualitative = type

Front 24

Quantitative variables _______ event, while qualitative variables ______ event.

Back 24

quantitative = quantify
qualitative = categorize

Front 25

What are constructs? aka?

Back 25

-internal attributes or characteristics (variables) that cannot be directly observed, but are useful for describing and explaining behaviour

-aka “hypothetical constructs”

Front 26

How can we measure a construct?

Back 26

-by observing and measuring behaviours that are representative of the construct
-the external behaviours can be used to create an operational definition of the construct

Front 27

What are the two components of an operational definition?

Back 27

-describes a set of operations for measuring a construct

-defines the construct in terms of the resulting measurements

Front 28

What are “real limits” for continuous variables?

Back 28

-the boundaries of intervals for scores that are represented on a continuous number line

-positioned exactly halfway between adjacent scores

Front 29

What is one key to determining whether a variable is continuous or discrete?

Back 29

-continuous variables can be divided into any number of fractional parts

-whenever you are free to choose the degree of precision or the number of categories for measuring a variable, it must be continuous

Front 30

What is an interval width?

Back 30

upper real limit – lower real limit

Front 31

What are the four scales of measurement, from simplest to most sophisticated?

Back 31

nominal, ordinal, interval, and ratio

Front 32

What is the nominal scale of measurement?

Back 32

-categories with different names (nominal = name)

-qualitative, not quantitative differences between categories

-no information on direction or size of difference between categories

Front 33

What is the ordinal scale of measurement?

Back 33

-named categories in an ordered sequence, ranked by size or magnitude

-know order (rank) of objects, but no info about size of intervals between ranks

-ie gold vs silver, we know one is better than the other, but not by how much

Front 34

What is the interval scale of measurement?

Back 34

-ordered categories with intervals of equal size

-no rational/absolute zero point (if you can have negative numbers, it’s an interval scale ie celcius)

-any time you are measuring a construct

Front 35

Can a construct be measured using a ratio scale?

Back 35

NO!

Front 36

What is the ratio scale of measurement?

Back 36

-ordered categories with intervals of equal size and rational (absolute) zero point

-basically interval scale + zero point (and not a construct)
-can form meaningful ratios

Front 37

What are the two distinct data structures used to classify different research methods and statistical techniques?

Back 37

-two different variables in same individual: correlational method

-two (or more) groups of scores: experimental and non-experimental methods

Front 38

Can we use the correlational method to produce non-numerical scores? How are they evaluated?

Back 38

yes, chi-square test

Front 39

What is the correlational method of observing variables?

Back 39

-observe two variables to see whether there is a relationship between them

-looking for things that change together

Front 40

What is the correlational method incapable of demonstrating?

Back 40

-an explanation of the relationship

-a cause-and-effect relationship

Front 41

What do the experimental and non-experimental methods compare?

Back 41

-two (or more) groups of scores to find differences between the groups

-one variable defines the groups, the other variable is measured to create scores for each group

Front 42

What is the goal of the experimental method?

Back 42

-to determine whether there is a cause and effect relationship between variables

-to show that changing the value of one variable will cause changes to occur in the second variable
-ie whether manipulating one variable causes another variable to change

Front 43

What is the difference between the experimental and non-experimental methods?

Back 43

-similar: both study two or more groups of scores to find differences between the scores BUT:

-experimental method includes manipulation and control

-non-experimental methods consist of nonequivalent groups and pre-post studies

Front 44

What two characteristics differentiate experiments from other types of research studies?

Back 44

-manipulation: of independent variable (dependent variable is measured to determine whether manipulation causes changes to occur)

-control: over research situation (to avoid confounding variables)

Front 45

What is an independent variable?

Back 45

-the variable being manipulated by the experimenter to create conditions or groups

-the treatment conditions to which subjects are exposed

-consists of antecedent conditions that were manipulated PRIOR to observing the dependent variable

Front 46

What is a dependent variable?

Back 46

-the variable being measured or observed (across all conditions or groups) to assess effect of treatment

Front 47

Lack of control leads to ____________.

Back 47

confounding variable, confound

Front 48

What is a confounding variable?

Back 48

-an uncontrolled variable that is unintentionally allowed to vary systematically with the independent variable

Front 49

What is confound?

Back 49

-being unable to tell whether changes in DV are due to IV or to confounding variable

Front 50

What are the two types of variables we need to consider for possible confounds?

Back 50

-participant variables: characteristics that are specific to an individual (ie age, intelligence, gender)

-environmental variables: characteristics of the environment (ie temperature, lighting, etc)

Front 51

What are the three basic techniques used to control other variables (possible confounding)?

Back 51

-random assignment: each participant has equal chance of being assigned to each group

-matching: to ensure equivalent groups or environments (ie equal IQ distribution)

-holding the variables constant (ie equal age for everyone)

Front 52

What is the control condition?

Back 52

-participants in the control condition do not receive the experimental treatment

-may receive no treatment, placebo, or be placed on a waiting list for treatment

-provides a baseline for comparison with the experimental condition

Front 53

When do we use the non-experimental method?

Back 53

-when we want to study the relationship between variables by comparing groups of scores BUT we cannot randomly assign participants to groups (lack of control) or manipulate the independent variable

Front 54

What are two common examples of non-experimental methods?

Back 54

non-equivalent groups and pre-post studies

Front 55

What are non-equivalent group studies?

Back 55

-non-experimental method comparing preexisting groups
-participants cannot be randomly assigned to groups because the grouping variable is a participant variable (ie gender, age, intelligence)

Front 56

What are pre-post studies?

Back 56

-non-experimental method comparing before and after scores

-in this case, time is the independent variable: experimenter has no control over the passage of time (or any variable that changes with time)

Front 57

What is the difference between a pre-post study and a correlational study?

Back 57

-they are similar in that both designs measure two scores for the SAME individual, however:

-correlational: the two scores correspond to two DIFFERENT variables (ie sleep time VS GPA)

-pre-post design: two scores correspond to SAME variable twice under two different conditions at different times

Front 58

What is a quasi-independent variable? Why is it not a TRUE independent variable?

Back 58

-the “independent variable” that is used to create the different groups of scores in a nonexperimental study

-not a true independent variable because it is not manipulated

Front 59

What do Σ and ΣX mean?

Back 59

Σ = summation, “the sum of” (sigma)

ΣX = “sum of the scores” (add all the scores for variable X)

Front 60

What are the rules of operation for the math in statistical notation?

Back 60

-brackets
-exponents
-division/multiplication
-***Σ (summation) using Σ notation***
-any other addition/subtraction

Front 61

What is the purpose of frequency distributions in organizing raw data?

Back 61

-provides an overview of the entire group of scores, making it easy to present an entire set of scores

Front 62

Descriptive statistics are a way of __________ and ___________ data.

Back 62

summarizing and organizing

Front 63

What are the three ways we can summarize/organize data?

Back 63

- tabular representation of data (frequency distributions)

- graphical representation of data (histograms, bar graphs, polygons)

-numerical representations of data (central tendency, )

Front 64

What is relative frequency? How do we calculate it? aka

Back 64

-aka proportion (p)

-what proportion (fraction) of the overall group is associated with each score

-proportion = p = f/n

Front 65

What are the groups in a grouped frequency distribution called?

Back 65

-class intervals

Front 66

What does a relative frequency distribution table look like?

Back 66

X f p=f/n %=p(100)

Front 67

What are apparent limits?

Back 67

-the limits that seem to denote the intervals in class intervals (in a group frequency distribution)

-they are called “apparent” because a interval of 60-64 would actually have real limits of 59.5-64.5 etc

Front 68

What are the steps to creating a group frequency distribution?

Back 68

1. Find the range of scores (largest - smallest + 1)

2. Determine the class intervals
--use about 10 groups or class intervals
--the width of a group or class interval should be a fairly simple number
--the bottom score of each class interval should be a multiple of the width
--all intervals should be the same width and cover the range of scores with no gaps

3. Create the grouped frequency table

Front 69

What is the difference between relative and cumulative frequency?

Back 69

-relative is proportion of each individual score, cumulative is added up (percentile ranks)

Front 70

Relative frequency indicates the ______________associated with each X value.

Back 70

proportion or percentage

Front 71

Percentile rank refers to a ___________ and percentile refers to a _______.

Back 71

-percentage (ie percentile rank of 60%)
-score (ie 60th percentile)

Front 72

What assumption is interpolation based on? Significance?

Back 72

-there is a constant rate of change from one end of the interval to the other

-values calculated are only estimates

Front 73

When constructing a frequency distribution table, how should the height of the Y axis compare to the width of the X axis?

Back 73

-the height should be approximately 2/3 to 3/4 of the length

Front 74

What graphs would be used for interval or ratio data?

Back 74

-histograms or polygons

Front 75

What are histograms?

Back 75

-use the frequency distribution in a simple or grouped frequency distribution table to form a graph

x-axis: extends to the real limits of the category (bars touch)

y-axis: frequency for each value

Front 76

How do we draw a histogram for data that has been grouped into class intervals?

Back 76

-width of the bar extends to the real limits of the interval

Front 77

What is a modified histogram?

Back 77

-drawing a stack of blocks above each score, each block representing one individual (blocks correspond to frequency)

-no need for a vertical line (y axis) showing frequencies

-provides a sketch, is not a substitute for an accurately drawn histogram

Front 78

What is the difference between a histogram and a bar graph?

Back 78

-histogram = interval or ratio data

-bar = nominal and ordinal data

-bar graphs are essentially the same as a histogram, except that spaces are left between bars

Front 79

Why do we use a bar graph instead of a histogram for nominal and ordinal data?

Back 79

nominal scale: the space emphasises that the categories are separate and distinct

ordinal: separate bars are needed because you cannot assume that the categories are all the same size

Front 80

How do you make a distribution polygon?

Back 80

-dots placed at
--exact value for single digit class intervals
--midpoint for grouped class intervals (but don’t show grouped intervals on the axis!)

-connect the dots

-connect the endpoints of the graph to the x axis at the next interval

Front 81

What are possible graphs to use for quantitative data? Qualitative?

Back 81

-quantitative: histogram, frequency polygon

- qualitative: bar chart, pie chart

Front 82

What three characteristics describe any frequency distribution?

Back 82

-shape, central tendency, variability

Front 83

Describe the three main classifications of distribution shape. (image)

Back 83

Front 84

What are the sample and population symbols for Mean?

Back 84

Front 85

What are the sample and population symbols for Variance?

Back 85

Front 86

What are the sample and population symbols for Standard Deviation?

Back 86

Front 87

When do skewed distributions often occur?

Back 87

-negatively skewed: may reflect a ceiling effect (can’t score any higher ie quiz out of 10)

-positively skewed: floor effect (can’t score any lower)

Front 88

What is the purpose of measuring central tendency?

Back 88

- to identify the “average” or “typical” individual by finding the single score that is most typical or most representative of the entire group

-we can use averages from two different groups to describe them and to measure the differences between them

Front 89

What are the 3 methods for determining central tendency?

Back 89

-mean, median, mode

Front 90

What is the mean?

Back 90

-the sum of all scores divided by the number of scores
-known as the “arithmetic average”
-population: represented by greek letter “mu” u
-sample: “X bar” (old way) or M

Front 91

Why is the mean known as the “balance point” of a distribution?

Back 91

-the sum of the negative deviations from the mean EXACTLY equals the sum of positive deviations from the mean

Front 92

What are the formulas for the mean?

Back 92

Front 93

What is the formula for weighted mean?

Back 93

Front 94

What is a weighted mean?

Back 94

- overall group mean (for more than one group)

-the combined sum divided by the combined n
--ie if 5 people average 50%, 10 people average 60%.. weighted mean is (250+600)/(5+10)

Front 95

How would adding, subtracting, multiplying, or dividing each score by a constant change the mean?

Back 95

-adding (or subtracting) a constant from each score changes the mean by THAT constant

-multiplying or dividing by a constant changes the mean in the SAME way

Front 96

What is the mode?

Back 96

-the category or score that has the greatest frequency

-the mode is the NAME of the category, not the frequency (even if the name is a number)

-can have more than one mode: 2 is bimodal, 3+ is multimodal

Front 97

Can a distribution have no mode?

Back 97

-yes, if either all the scores are the same or sometimes if there are several scores with equally high frequencies

Front 98

Can a distribution have two modes that don’t have identical frequencies?

Back 98

-occasionally, if there are two separate and distinct groups (ie up down up)

-taller peak = major mode, shorter peak = minor mode

Front 99

What is the median?

Back 99

-the score that divides the distribution in half so that 50% of the individuals in a distribution have scores at or below the median

-equivalent to the 50th percentile

Front 100

What does the median tell us about the shape of the distribution?

Back 100

-NOTHING

Front 101

How can mean and median both measure the “middle” of the distribution?

Back 101

-median defines middle in terms of scores (same number of scores below as above)

-mean defines middle in terms of distance (same total distance below as above)

Front 102

What measures of central tendency can you find for nominal scales?

Back 102

mode

Front 103

What measures of central tendency can you find for ordinal scales?

Back 103

mode and median (NOT mean)

Front 104

What measures of central tendency can you find for interval scales?

Back 104

all three

Front 105

What measures of central tendency can you find for ratio scales?

Back 105

all three

Front 106

In a normally shaped distribution (perfectly symmetrical) what measures of central tendency will always be the same?

Back 106

-all three: mean, median and mode

Front 107

In a symmetrical distribution, what measures of central tendency will always be the same?

Back 107

-mean and median

Front 108

What does a normally shaped distribution look like?

Back 108

Front 109

Where would the mean, median, and mode fall (relative to each other) in a skewed distribution?

Back 109

Front 110

What measure of central tendency is most affected by skew?

Back 110

-mean, because it accounts for ALL scores

Front 111

in a skewed distribution, the mean is ______________.

Back 111

-pulled toward the tail

Front 112

What is an outlier?

Back 112

-an extreme score

-lies apart from most of the other distribution

-several outliers in a distribution gives a skewed shape

-outliers pull some central tendency measures with them

Front 113

What is usually the preferred measure of central tendency (according to text)? Why?

Back 113

-mean

-uses every score in the distribution (typically gives good representative value)

-also closely related to variance and standard deviation

Front 114

When do we use a mean? (4 points)

Back 114

-you have interval or ratio data

-the distribution is normally shaped

-there is no missing data

-the distribution is closed-ended (ie no “3 or more” values)

Front 115

In what situations does the median serve as a valuable alternative to the mean?

Back 115

-extreme scores or skewed distributions (mean can be distorted/displaced)

-undetermined values, ie missing data (mean impossible to determine)

-open ended distributions (mean impossible to determine)

-ordinal scale (mean not accurate/appropriate because ordinal scales don’t measure distance)

Front 116

In what situations does the mode serve as a valuable alternative to the mean?

Back 116

-nominal scale (mean and median impossible to determine)

-discrete variables (mean is possible, but seems unrealistic if fractional)

-describing shape (easy way to help visualize when given along with median or mode)

Front 117

What is variability?

Back 117

-measure of the degree to which scores are spread out or clustered together

Front 118

What two purposes does a good measure of variability serve?

Back 118

1. Describes the distribution: clustered vs spread out

2. Measures how well an individual score (or group of scores) represents the entire distribution (read: how much error to expect if you are using a sample to represent a population)

Front 119

What are the three measures of variability (that we talk about)?

Back 119

-range, variance, and standard deviation

Front 120

What is range (in words)?

Back 120

-measure of variability

-the distance between the smallest and largest observations (using RLs if continuous)

Front 121

What kinds of variables can range be calculated for?

Back 121

-interval and ratio (because the concept of distance applies to them)

Front 122

What is variance (in words)?

Back 122

-a measure of variability

-the average of all squared deviations (distances) from the mean

Front 123

what is standard deviation (in words)?

Back 123

-measure of variability

-rough measure of the average amount by which observations deviate from the mean

-square root of the average squared deviation (undoes the squaring)

Front 124

What is the goal of variance and standard deviation?

Back 124

-to measure the typical distance that scores are from the mean

Front 125

hat do we calculate the range value of variability?

Back 125

continuous: range = URL Xmax – LRLXmin

discrete: count the categories (or largest – smallest + 1)

Front 126

The deviation is the ________ and _________ from the mean.

Back 126

distance (#), direction ( + or -)

Front 127

What are the steps to finding the standard deviation, in words? (by definition, NOT actual calculation)

Back 127

1. Find the deviation for each individual score

2. Square each deviation score

3. Add the squared deviation scores (SS = sum of squares)

4. Divide sum of squares by number of scores (variance)

5. Find the square root of the variance

Front 128

Why do we need to square each deviation score to find standard deviation?

Back 128

-we want a single number that represents the variability in the distribution, but if we add up all the deviations from the mean, the sum (and thus the mean deviation) would always equal 0

-squaring each deviation score gets rid of the + and – signs, which cancel each other out

-result is the mean SQUARED deviation (aka variance)

Front 129

if we sum the deviations from the mean for a given distribution, the total will always ______.

Back 129

equal zero

Front 130

What is Sum of Squares?

Back 130

-SS

-the sum of the squared deviation scores

Front 131

What is the definitional formula of the sum of squares?

Back 131

-direct, but can become complicated if scores aren’t whole numbers

-replace u with M for sample

Front 132

What is the computational formula for the sum of squares?

Back 132

-works with the scores (and not deviations from the mean), so it reduces complications of decimals

-replace N with n for sample

Front 133

What is the variance formula for a population?

Back 133

Front 134

What is the variance formula for a sample?

Back 134

Front 135

Why is a sample a biased estimate of a population’s variability? How do we adjust for this bias?

Back 135

-because any sample will be less variable than its parent population

- adjust for this by using n-1 instead of n when calculating sample variance and standard deviation

-the effect of this adjustment is to increase the value obtained

Front 136

Why do we use 1 (and not another number) when adjusting for the bias of the sample’s variability compared to the population?

Back 136

-because a sample of n scores has a “degree of freedom” of 1 for the sample variance, which means that while the rest of the scores are independent and free to vary, ONE score is restricted

-for example, if a sample has 3 scores and we know the mean, two of the scores can be ANY number, but the last number must be the exact number that will result in the correct mean

Front 137

How does standard deviation describe variability?

Back 137

-measuring the distance from the mean, accounting for the ends of the distribution and clustering of scores

Front 138

What is the standard deviation formula for a population?

Back 138

Front 139

What is the standard deviation formula for a sample?

Front 140

What is the standard deviation formula for a sample?

Back 140

Front 141

In a graphic representation of the mean and standard deviation, where should the standard deviation line be?

Back 141

-extend approximately halfway from the mean to the most extreme score

Front 142

What makes a sample statistic biased or unbiased?

Back 142

-biased: if it tends to under or overestimate the corresponding population parameter

-unbiased: if the average value of the statistic is equal to the population parameter

Front 143

Is sample mean a biased or unbiased statistic?

Back 143

-unbiased (best estimate of the population mean)

Front 144

Is sample variance a biased or unbiased statistic?

Back 144

-unbiased (best estimate of the population variance) ***IF n-1 IS USED***

Front 145

Is sample standard deviation a biased or unbiased statistic?

Back 145

-biased (tends to UNDERESTIMATE the population standard deviation)

Front 146

What are the most common values used to describe a set of data?

Back 146

-mean and standard deviation

Front 147

As a rule of thumb, what percentage of scores will be within 1 standard deviation of the mean? 2 standard deviations?

Back 147

-1: roughly 70%

-2: roughly 95%

Front 148

What happens to standard deviation when you add a constant to each score? Explain.

Back 148

-does not change, there is still the same distance between scores

Front 149

What happens to standard deviation when you multiply or divide each score by a constant?

Back 149

-changes SD in the same way

Front 150

Can a sum of squares be negative?

Back 150

No!

Front 151

What two considerations determine the value of any statistical measurement?

Back 151

1. The measure should provide a stable and reliable description of the scores (not be greatly affected by minor details in the set of data)

2. The measure should have a consistent and predictable relationship with other statistical measurements

Front 152

What factors affect variability?

Back 152

1. Extreme scores
-all measures are affected, but particularly range

2. Sample size
-range is directly affected, SD and variance are relatively unaffected

3. Stability under sampling
-SD and variance are said to be stable under sampling, range is unstable

4.Open-ended distributions
-cannot compute range, SD or variance.. can only use semi-interquartile range (which we don’t discuss!)

Front 153

What is the process of standardizing distributions, in simple terms?

Back 153

-takes different distributions and makes them equivalent (comparable)

Front 154

What are the benefits of using the mean and standard deviation together?

Back 154

-efficient way to describe a distribution with just two numbers

-allows a direct comparison between distributions that are on the same scales

Front 155

The relative position of an X value within the distribution of scores depends on the distribution’s ____ and ____________.

Back 155

mean, standard deviation

Front 156

What are raw scores?

Back 156

-the scores that are a direct result of measurement

-they are in the original units of measurement

Front 157

What is the purpose of a z-score?

Back 157

-to identify and describe the exact location of every score in a distribution

-to standardize a distribution (ie lots of tests for IQ, but mean IQ is always 100)
--allows direct comparison with other distributions with z-transformed scores

Front 158

What is a Z score?

Back 158

-combines a score, the mean, and the SD into a single number that precisely describes its location relative to the other scores in the distribution

-tells us whether a score is above or below the mean, and by how many standard deviations

Front 159

z-scores are measured in units of ___________.

Back 159

standard deviation

Front 160

What is the z score formula?

Back 160

Front 161

What is the formula for finding X from a z score? (ie when you know mean, z-score, and SD)

Back 161

Front 162

Why does distribution shape stay the same after we standardize (transform to z-scores)?

Back 162

-to find z-scores, we subtract by a constant and divide by a constant

-scores don’t change position, just change units (original become SD)

Front 163

If we transform every X value in a distribution (raw scores) into a z-score, what happens to the shape, mean, and standard deviation?

Back 163

-shape: no change

-mean: becomes 0

-standard deviation and variance: become 1

Front 164

Why is the mean of any distribution of Z scores zero?

Back 164

-any score equal to the mean is 0 standard deviations away from the mean

-the sum of all positive Zs must equal the sum of all negative Zs

Front 165

The standard deviation of any distribution expressed in z-scores is always ____, which means that the variance is always ____ because...

Back 165

both 1, because the square root of 1 is 1, 1 squared is 1

Front 166

What is a “standardized distribution”? Used for?

Back 166

- distribution composed of raw scores that have been transformed to create predetermined (known) values for mean and SD

-used to compare distributions across different measures

Front 167

-A z-score distribution is an example of a ____________ distribution.

Back 167

-standardized distribution (mean = 0, SD = 1)

Front 168

What is the z-score distribution also known as? Why?

Back 168

- the standard normal distribution

-because it is a normal distribution expressed in standard (Z) scores (mean = 0, SD = 1)

Front 169

How do we transform z-scores to a distribution of our choice?

Back 169

1. transform your raw scores into z scores

2. Transform z scores into the new distribution using the formula Xnew = μ(new) + zσ(new)

Front 170

What are representative and extreme z scores, according to the textbook?

Back 170

-representative/central = close to the mean (within one SD?)

-extreme = 2 or more SD’s away from the mean