Research Methods Exam 2

Front 1

an idea constructed by the researcher to explain observed events (a rational idea that is used to explain a phenomenon); an operational definition (explanatory idea about internal states that generate behavior)

Back 1

construct

Front 2

How well the study’s results support the theory or
constructs behind the research; Whether the theory supported by the findings
provides the best available explanation of the
results

Back 2

construct validity

Front 3

How do we maximize construct validity?

Back 3

The researcher should use clearly stated
definitions and carefully build hypotheses on
solid, well‐validated constructs which have
received support from numerous other
studies; have a clear definition of the constructs of interest; verify that there is a good match between constructs and operations used to represent them

Front 4

Defined as the degree of confidence that the
inferences about study outcomes based on
statistical tests are correct

Back 4

statitical conclusion validity

Front 5

What are the factors affecting validity types?

Back 5

-measurements must be reliable and valid
-useful measures are sensitive
-useful tests are specific
-should avoid ceiling and floor effects
-researcher bias
-

Front 6

refers an effect whereby data
cannot take on a value higher than some
"ceiling."

Back 6

celing effect

Front 7

refers an effect whereby data
cannot take on a value higher than some
"floor."

Back 7

floor effect

Front 8

How can researcher bias be lessened?

Back 8

-using designs where the researcher is blind to conditions
-comparisons cross-checked for consistency in recordings

Front 9

An ________ _________ is a measure of the
“influence” of a journal
• Measures the ___________ with which the
"average article" in a journal has been cited in
a given period of time
• Journal Impact Factors are produced by
.....?
• Impact factor available thru (1) _______________ ____________
(2) on a journal’s _________ _________

Back 9

impact factors
frequency
Thomson Institute for Scientific Information
University Libraries; journal's website

Front 10

Journals which publish on more specialized
topics will have _______ impact factors than
those which publish on more general topics

Back 10

lower

Front 11

Other factors influencing impact factors?

Back 11

Date of publication
– Size of journal
– Average number of citations
– Number of review articles
– Type of field

Front 12

We’ve now learned about ________ of evidence
and ___________ of evidence in terms of:
– Study design (experiment, quasi‐experiment, etc.)
– Validity (internal, external, construct, etc.)
• The highest quality, strongest and/or most
innovative articles will often be the most
highly influential
– Journal impact factors!

Back 12

quality; strength

Front 13

What section of a research paper summarizes data and statistical analysis? It uses _____________ statistics and includes tables and figures.

Back 13

results section
descriptive

Front 14

_____________ statistics summarize, simplify,
and describe measurements
– Measures of “_______ __________: mean, median,
mode
– Measures of ___________: range, variance,
standard deviation

Back 14

descriptive
central tendency
variability

Front 15

____________ statistics help researchers
interpret the data

What are some examples?

Back 15

inferential
T‐tests, ANOVA, ANCOVA, Wilcoxon sign‐rank
test, Tukey’s HSD, etc.

Front 16

The appropriateness of the descriptive and
inferential statistics for data affect _____ _________ __________

Back 16

statistical conclusion validity

Front 17

What are the different scales of measurement?

Back 17

Nominal Scale
• Ordinal Scale
• Interval Scale
• Ratio Scale

Front 18

Naming scale
– Each number reflects a category

What are some examples?

We can only look at _____________, can't ____ or __________

Back 18

nominal

men/women, type of hearing loss
(conductive, sensorineural, mixed); desire to upgrade
to AuD (yes/no); code as 1, 2, 3, etc.; phoneme
production (correct/incorrect); diagnostic category
(stutterer/nonstutterer)

frequencies; add/subtract

Front 19

Scale indicating rank order
– Reflects the order, but not the amount

What are some examples?

We can calculate the _________ rankin but not much more.

Back 19

ordinal

ranked severity groups,
socioeconomic status (low‐, middle‐, upper
class), rank in class, stimulus complexity
(easy, moderate, difficult)

mean

Front 20

can only be represented
by integers (whole numbers like 1, 2, 3…)
– Nominal and ordinal data

theoretically capable
of taking on fractional units of measurement –
decimals (sometimes rounded)
– Interval and ratio data

Back 20

discrete variables

continuous variables

Front 21

Scale with equal intervals
–The scale indicates amount, but
with no zero point

What are some examples?

We can ....

Back 21

interval scales

temperature on the
Celsius scale, most assessments
(TOLD, PPVT‐R, CELF)

Can add subtract, but typically
not multiply or divide

Front 22

Scale that fits the number system well
– Includes equal intervals and a true zero ‐ “zero
means zero” ; "score" data

Examples...?

We can...

Back 22

ratio scale

7
Ratio Scales
• Scale that fits the number system well
– Includes equal intervals and a true zero ‐ “zero
means zero”
• Examples: time, distance, frequency, weight,
volume, etc., vowel duration, sound frequency,
air pressure, number of misarticulations,
diadochokinetic rate, speech intelligibility score

Perform any mathematical equation

Front 23

__________: Experiments, comparative studies
____________: Correlational studies, survey
research, retrospective research
_______: Observational research, case studies,
Interview research*
________: Interview research*, narrative
research, testimonials
*Strength is higher if structured interviews are used

Back 23

strong
medium
low
lowest

Front 24

2‐D graphs have:
– An_________ (abscissa)
– A ________ (ordinate)

Back 24

x-axis
y-axis

Front 25

Frequency or grouped frequency can be
represented as either:
___________ Bar graph showing a count on the y‐axis
– _______ __________: Line graph showing a count on the yaxis

Back 25

histogram
frequency polygon

Front 26

shows the %
or proportion of scores that fall within each
range of values
– Similar to histogram, but y‐axis is different

Back 26

relative frequency distribution

Front 27

whole pie is 100%; pieces of pie
represent fractions of 100% for each category

Back 27

pie chart

Front 28

Some distributions are ______________ (right side is the
mirror image of the left side)
– E.g., ________ distributions: “bell curve”
• Other distributions are not symmetric
– ___________ _________: “Tail” tends toward positive direction
– ___________ ____________: “Tail” tends toward negative direction

Back 28

symmetric
normal
positively skewed
negatively skewed

Front 29

We may want to know where “most of the
participants fall” on the distribution
– This is called the _________ ___________ of the
distribution
– Measures of central tendency include __________ , ____________, ___________

Back 29

central tendency

mean median mode

Front 30

Central tendency = _______ of a distribution
• Measures of central tendency:
– _______ (average)
– ___________ (middle score in rank‐ordered list)
– _______ (most frequent score)

Back 30

mean
median
mode

Front 31

___________ – one most frequency score
__________ – two most frequency scores

Back 31

unimodal
bimodal

Front 32

We can quantify __________(or horizontal spread)
of a distribution by using.... (2)
____________ = (standard deviation)2
(square of standard deviation)
___________ _____________ = (variance)
(square root of the variance)

Back 32

variability
variance & standard deviation
variance
standard deviation

Front 33

A common type of unimodal distribution is the
____________ distribution

Back 33

normal

Front 34

To calculate the variance, follow these steps:

Back 34

– Work out the mean (average of the numbers)
– Then for each number: subtract the mean and
square the result (the squared difference)
– Then work out the average of those squared
differences
This gives you the variance!
– Standard deviation is the square root of the
variance

Front 35

In a histogram, counts go on the ____ axis.

Back 35

y-axis

Front 36

__________ statistics summarize, simplify,
and describe measurements. They include measures of ____________ __________ (mean, median, and mode; and measures of _______________: range, variance, SD.

Back 36

descriptive statistics
central tendency
variability

Front 37

____________ statistics help researchers
interpret the data. These include: T‐tests, ANOVA, ANCOVA, Wilcoxon sign‐rank
test, Tukey’s HSD, etc.

Back 37

inferential statistics

Front 38

_________ _________ take into account (a) group mean differences relative to (b) ____________ and (c)

Back 38

test statistics
variability
number of participants

Front 39

_____________ ______________ statistics provides a standard way of telling whether differences in means across levels of the IV(s) outweigh variability in each level relative to number of participants
• If the inferential test statistic passes a
specified __________, then there is a significant effect of that variable; otherwise there is not a significant effect.

Back 39

inferential statistics
threshold

Front 40

If there is statistical __________, then there’s no
significant effect of the IV(s)
If there is statistical __________, then there is a
statistically significant effect of the IV(s)

Back 40

equality
difference

Front 41

What are examples of some test statistics?

Back 41

t scores (t-tests), z scores (z-tests), F (for ANOVA)

Front 42

“Cutoff value” specifies the boundary between an extreme score from a non‐extreme score.
If a test statistic is in the “_________ __________ region”, then
we conclude the IV had a statistically significant effect

Back 42

extreme score region

Front 43

What are the locations of the critical region boundaries for three different levels of significance? .

Back 43

α = .05,
α = .01, and α = .001

Front 44

Descriptive statistics for a sample are referred to as _________ ___________.
– Mean (M), standard dev (s), variance (s2)

Back 44

sample statistics

Front 45

_________ ___________are abstract or “ideal” values of sample statistics
– Mean (μ), standard dev (σ), variance (σ2)

Back 45

population parameters

Front 46

What is the first step in the logic of inferential stats tests? What are the 2 possibilities? These are stated in terms of ___________ _______________ like μ, σ.

Back 46

Identify & state each hypothesis.

The levels of the IV are equal
The levels of the IV are not equal

population parameters

Front 47

What is step 2 in the logic of inferential stats tests?

Back 47

Set the criteria for a decision:

2 substeps:
- determine the statistical test
- determine the cutoff value for the distribution that separates normal from extreme scores

Front 48

___________ _______________ refers to how subjects are chosen to be part of a study. It refers to drawing a sample from the population in a manner that ensures that each member of the population has an equal change of being chosen for the sample.

Back 48

random sampling

Front 49

________________ ____________ is a requirement of a true experiment in which an experimental group and a control group are formed by the experimenter. It is achieved when each subject is just as likely to be assigned to the control group as the experimental group.

Back 49

random assignment

Front 50

Research that focuses primarily on theory testing is often called _________ research. _____________ research seeks to go from data directly to a real-world application.

Back 50

basic
applied

Front 51

What is the college sophomore problem?

Back 51

The worry that, because college sophomores are the subjects in an extremely large number of psychological investigations, the generality of the results is in question.

Front 52

What is the idea that a new theory in science must make contact with previously established empirical facts? To be considered an advance, it must not only explain new facts, but also account for old ones.

Back 52

principle of connectivity

Front 53

The principle of __________________________ involves scientists and those who apply scientific knowledge making a judgement about where the preponderance of evidence points. It is a useful tool for the lay consumer of scientific information and particularly useful in evaluating psychological claims. It urges us to base conclusions on data that arise from a number of slightly different experimental sources.

Back 53

converging evidence

Front 54

A design known as the _____________ has been used to investigate the televised-violence/aggressive behavior issue.

Back 54

field experiment

Front 55

The ______________________________ refers to the failure of some people to respond with help when observing another individual in an emergency situation.

Back 55

unresponsive bystander phenomenom

Front 56

To conclude that there is a significant causal relationship between variable A and behavior B does not mean that variable A...

Back 56

is the ONLY cause of behavior B

Front 57

A factor that influences behavior may have different effects when operating in conjunction with another factor compared to when it is acting alone. This is called the concept of _____________. The magnitude of one variable may depend on the level of another.

Back 57

interaction

Front 58

____________________ means that it is more likely than not, but does not hold true in all cases.

Back 58

probabilistic trend

Front 59

The prediction of outcomes based on group characteristics is called ______________________.

Back 59

aggregate/actuarial prediciton

Front 60

_____________________ is the tendency for people to see links between events in the past and events in the future when the two are really independent.

Back 60

gambler's fallacy

Front 61

What is important when dealing with multiple causation?

Back 61

interactions

Front 62

For IV #1... (A,B)

What does H0 say? What is the equation that illustrates this?
What does H1 say? What is the equation that illustrates this?

Back 62

“The levels of the IV are statistically equal
- There is no difference between the levels”
H0: uA = uB
"The levels of the IV are statistically
nonequal - There is a difference between the
groups”
H1: uA ≠ uB

Front 63

What is step 3 in the logic of inferential stats tests?

Back 63

compute test statistics from the actual data

Front 64

What is step 4 in the logic of inferential stats tests?

Back 64

make a statistical decision and interpret the results relative to the research question.

Front 65

What are the potential benefits of studying more than one IV in a single study?

Back 65

-time/effort/cost
-possibility of looking for interactions among different variables

Front 66

An unexpected pattern of data
in which one or more condition(s) do not
follow the trends seen for the individual IV’s
in the rest of the study

Back 66

interaction

Front 67

A study that concludes that there are very different outcomes for men and women under different drug dosages revealed the ____________ of IV of gender and IV of drug dosage. Thus, the ________ of drug dosage depends on whether the participant is male or female.

Back 67

interaction

Front 68

Interactions become clear when studying two IVs simultaneously. This is done using a ____________ study, or one that has more than 1 IV.

Back 68

parametric

Front 69

________________ is a study where there is more than one IV and there are participants in all possible pairings of levels of the IV’s. These are the norm in studies with more than 1 IV.

Back 69

factorial

Front 70

First, determine how many ______ there are. This will tell you how many possible ___________________ and _______________ there are. For each, there will be a separate ________________.

Back 70

IVs
main effects
interactions
test statistic

Front 71

A single IV will have...

Two IV’s have...

Back 71

one pair of hypotheses for each DV

3 pairs of hypotheses for each DV
(one relating to possible main effect of IV 1, one for main effect of IV 2, one for interaction of IV 1 and 2

Front 72

In an experient w/ one IV ; three levels (multivalent study - experiment or quasiexperiment):
Level A, Level B, Level C
– H0: ““The levels of the IV are statistically equal
- There is no difference among the groups”
H0: uA = uB = uC
– H1: “The levels of the IV are statistically
nonequal - There is a diff. among the groups”
H1: uA ≠ uB and/or uB ≠ uC and/or uA ≠ uC (*post-hoc test needed)

Back 72

lala

Front 73

1. One IV with two levels (bivalent
study - experiment or quasi-experiment):
Level A and Level B
– H0: “The levels of the IV are statistically equal -
There is no difference between the levels”
H0: uA = uB
– H1: “The levels of the IV are statistically
nonequal - There is a diff. between the groups”
H1: uA ≠ uB

Back 73

lala

Front 74

Interaction between IV #1 and IV #2:
– H0: “IV #1 affects all levels of IV #2 in a
statistically equal manner”
– H1: “IV #1 affects one or more levels IV #2 in a statistically unequal manner”
– No equations required

Back 74

lala

Front 75

Cutoff values depend on ________________________ given by:
– Number of ____________ AND/OR
– Number of ________________

Back 75

degrees of freedom

participants
levels of the IV

Front 76

This type of statistical test is based on the assumption that data fit a particular shape or distribution (e.g., ‘Gaussian distribution’). What are some examples?

_____________ tests are not based on this assumption.
– Examples: Chi-squared test, Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test

Back 76

parametric

t-test, ANOVA (f)

nonparametric

Front 77

If you have one IV with two levels, you use a ...

If you have one IV with three or more levels, you use a(n)...

If you have two or more IVs, you use...

Back 77

t-test (or ANOVA)

ANOVA

ANOVA

Front 78

What is the "extreme" region of a distribution?

Example: With df = 20, the critical t value is +/-2.09.
If we get t(20) = 5.73, it’s significant! (p < .0001)

Back 78

5% or smaller portion of the curve

Front 79

What are the (3) types of causation?

_______________: Where a variable is necessary and sufficient to produce an effect on a DV

_________________: A causal variable might be necessary for an effect on the DV, but the effect depends on another variable.

______________: A causal variable might be neither necessary nor sufficient, but its presence increases the statistical probability of an effect.

Back 79

strongest form

weaker form

weak form

Front 80

Any variable that confounds the ability of the chosen sample to represent the population parameter from which it was drawn (bias in representation due to selection, affects external & internal validity __________________________

Back 80

selection bias

Front 81

How is selection bias minimized?

Back 81

-Using random assignment to the levels of IV
-Also by matching participants in terms of groups
-Developing well-defined inclusion criteria
-Develop exclusion criteria to eliminate unwanted participants that might bias a sample
-Sampling as close to randomly as possible

Front 82

Defined as validity of the inference about
whether the causal relationship holds over
people, settings, treatment variables,
measurement variables, and time ________________________

Back 82

external validity

Front 83

What are the (4) types of external validity?

Back 83

population validity
treatment variation validity
temporal validity
ecological validity

Front 84

__________ describes an all‐inclusive data set about which researchers want to draw a conclusion.
A _________ is just a subset of the population.
There will inevitably be some amount of
______________, i.e., difference between the measures collected for a sample and the population it’s believed to represent

Back 84

population
sample
sampling error

Front 85

This refers to the ability to generalize from the sample in a study to a larger population. The _____________ population is the larger population to whom the results are generalized. The ____________________________ population is the one that is available to the researcher.

Back 85

population

target population

experimentally accessible population

Front 86

How can generalization be achieved easily?

Back 86

If the researcher uses random selection

Front 87

This involves drawing observations from a population so that each individual has an equal chance of being selected _________________. This is different than __________________, which sets out to create equivalent groups by balancing them based on specific characteristics.

Back 87

random sampling
random assignment

Front 88

____________________ is a sampling method where each individual has an equal probability of being selected, "true random selection."

Back 88

simple random sampling

Front 89

___________________ involves dividing a population into subgroups called strata to assure that certain segments of the population are adequately represented in a sample. ______________ means a subdivision/group.

Back 89

stratified sampling

strata

Front 90

________________________ refers to the extent to which results of the experiment can be generalized across time.

Back 90

temporal validity

Front 91

____________________ refers to the generalizability of results across variations in treatment. It's an issue because the administration of the treatment can vary from one administration to the next.

Back 91

treatment variation validity/transferability

Front 92

______________________ refers to the degree to which behaviors that are observed in a study reflect the behaviors that occur in natural settings.

Back 92

ecological validity

Front 93

Refers to the extent that causal inferences are justified based on observed changes in a DV in response to systematic variations in an IV

The strength of depends on to
what extent extraneous variables have been removed from the study.

Back 93

internal validity

Front 94

An most important possible threat to internal
validity is not having a high enough constraint
study design

How do we minimize this?

Back 94

study design threat

random assignment
manipulation to remove extraneous variables

Front 95

The fact that an individual changes/matures over time

What is the problem with this?

Back 95

Maturation

There is no control group to compare for effects of natural maturation

Front 96

Effects of previous testing on performance

What can we do to guard against this?

Back 96

testing effects

use diff questions/assessment
wait a long time before reassessing

Front 97

A change in a DV due to a tx effect that is not immediately observable but tends to become increasingly observable over a span of time

To guard against this...?

Back 97

sleeper effect

plan appropriate treatment interval and track participants after study has ended

Front 98

This is any event between the beginning of a treatment and the measurement of the DV that could produce the outcome

How can we minimize this?

Back 98

history

make sure no new events occur after beginning of tx

Front 99

Refers to the fact that some individuals 'drop out' before completing a study

Ways to minimize?

Back 99

attrition

give incentives for participating, enroll new participants in the study