Research Methods Study Guides > Key terms > Flashcards

Key terms Flashcards

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1
Q

Attrition

A

A threat to internal validity caused by differential dropout rates across conditions.

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2
Q

Bar graph

A

A graph for nominal or ordinal data where bars do not touch, representing discrete categories.

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3
Q

Between-subject design

A

An experimental design where different participants are assigned to each condition.

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4
Q

Central tendencies

A

A statistical measure to determine a single score that defines the center of a distribution. The goal is to find the single score that is most typical or representative of the entire group.

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5
Q

Cluster Sampling

A

A probability sampling method where entire groups (clusters) are randomly selected, and all members of the selected groups are used.

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6
Q

Cohen’s d

A

A measure of effect size that expresses the difference between two group means in standard deviation units.

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7
Q

Continuous variable

A

A variable for which there are an infinite number of possible values between any two observed values (e.g., height, time).

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8
Q

Convenience Sampling

A

A non-probability sampling method where available and willing participants are selected.

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9
Q

Correlational Design

A

A non-experimental design that examines the associations between variables as they naturally occur, without manipulation.

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10
Q

Cronbach’s Alpha

A

A measure of internal consistency reliability, indicating how well items within a scale measure the same construct.

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11
Q

Cumulative Frequency (cf)

A

The sum of the frequencies at a particular score plus all the frequencies below it.

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12
Q

Dependent Variable

A

The variable that is the measured outcome in a study.

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13
Q

Descriptive Stats

A

Statistical procedures used to summarize, organize, and simplify data.

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14
Q

Discrete Variables

A

A variable consisting of separate, indivisible categories. No values can exist between two neighboring categories.

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15
Q

Effect Size

A

A measure of the magnitude of a research finding, indicating how large the effect is.

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16
Q

Experimental Design

A

A research design characterized by random assignment, manipulation of an independent variable, and control of extraneous variables, allowing for causal inference.

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17
Q

External Validity

A

The degree to which the results of a study can be generalized to other participants, settings, and times.

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18
Q

Frequency (f)

A

The count of scores in each category of a measurement scale.

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19
Q

Frequency Distribution

A

An organized tabulation showing the number of individuals located in each category on the scale of measurement.

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20
Q

Frequency Polygon

A

A graph for interval or ratio data where dots are placed above the center of each interval and connected by lines to show the shape of the distribution.

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21
Q

Histogram

A

A graph for interval or ratio data where the height of each bar corresponds to the frequency and the width corresponds to the real limits of the score, with bars touching.

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22
Q

History

A

A threat to internal validity where external events occurring between measurements affect the outcomes.

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23
Q

Hypothesis Testing

A

A statistical framework for using sample data to evaluate a hypothesis about a population.

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24
Q

Independent Variable

A

The variable that is manipulated by the researcher.

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25
Instrumentation
A threat to internal validity where the measurement tools or procedures change over the course of a study.
26
Inferential Stats
Statistical techniques that use sample data to draw conclusions about populations.
27
Inter-rater reliability
The degree of agreement between different raters or observers measuring the same behavior.
28
Internal Consistency
A type of reliability that assesses how well items within a single scale measure the same construct.
29
Internal validity
The extent to which a study produces a single, unambiguous explanation for the relationship between two variables.
30
Interval scale
A scale of measurement with ordered categories and equal intervals between values, but no true zero point (e.g., temperature in Celsius).
31
Kurtosis
The degree of peakedness of a distribution (leptokurtic, mesokurtic, platykurtic).
32
Least Squares Criterion
The principle used in linear regression to find the best-fitting line by minimizing the sum of the squared differences between actual Y values and predicted Y values.
33
Leptokurtic
A distribution that is more peaked than a normal curve and has heavy tails.
34
Maturation
A threat to internal validity referring to natural changes in participants over time that may affect outcomes.
35
Mean
The measure of central tendency calculated by summing all scores and dividing by the number of scores; the "balance point" of a distribution.
36
Median
The midpoint of a distribution when scores are ordered from lowest to highest.
37
Mesokurtic
A distribution with a shape corresponding to a normal curve.
38
Mixed Design
An experimental design that combines both between-subjects and within-subjects factors.
39
Mode
The most frequently occurring score or category in a distribution.
40
Negatively skewed
A distribution where the tail extends to the left (negative end), with most scores clustered at the high end.
41
Nominal Scale
A scale of measurement consisting of categories that are differentiated only by name, with no quantitative distinction or order (e.g., gender).
42
Non-experimental Design
A research design, such as a correlational study, that does not involve manipulation of variables.
43
Non-Probability Sampling
A sampling method where the odds of selecting a particular individual are unknown because the selection process is not random.
44
Normal Distribution
A specific symmetrical, bell-shaped distribution with the greatest frequency in the middle.
45
Null hypothesis
The hypothesis stating that in the general population, there is no change, no difference, or no relationship.
46
Ordinal Scale
A scale of measurement consisting of a set of categories that are organized in a ranked order (e.g., class rank).
47
p-value
The probability of obtaining a result as extreme as or more extreme than the one observed, assuming the null hypothesis is true. A value less than .05 is typically considered statistically significant.
48
Parameter
A value, usually numerical, that describes a population (e.g., μ, σ).
49
Pearson r
A measure of the strength and direction of a linear relationship between two quantitative variables.
50
Platykurtic
A distribution that is flatter than a normal curve.
51
Population
The set of all individuals of interest in a particular study.
52
Positively Skewed
A distribution where the tail extends to the right (positive end), with most scores clustered at the low end.
53
Probability Sampling
A sampling method where each individual in the population has a known, specifiable probability of being selected.
54
Quasi-experiment
A research design where groups are formed based on pre-existing characteristics (e.g., gender, age) rather than random assignment.
55
Ratio scale
An interval scale with the additional feature of an absolute zero point, allowing for meaningful ratios (e.g., height, weight).
56
Real limits
For a continuous variable, the boundaries located exactly halfway between adjacent scores.
57
Regression to the mean
A threat to internal validity where extreme scores on a first measurement tend to move closer to the average on a second measurement.
58
Reliability
The consistency or stability of a measurement.
59
Representative Sample
A sample with the same characteristics as the population from which it was selected.
60
Sample
A set of individuals selected from a population, usually intended to represent the population in a research study.
61
Sampling error
The natural discrepancy, or amount of error, between a sample statistic and its corresponding population parameter.
62
Scales of measurement
The set of four scales (nominal, ordinal, interval, ratio) that classify variables based on their properties.
63
Selection
A threat to internal validity where groups differ systematically before the study begins.
64
Selection Bias
Occurs when participants are selected in a way that increases the probability of obtaining a biased sample.
65
Simple Linear Regression
66
A statistical procedure for finding the best-fitting straight line (Ŷ = a + bX) for a set of data to predict one variable from another.
67
Simple Random Sampling
A probability sampling method where each individual has an equal and independent chance of being selected.
68
Skewness
The degree of asymmetry in a distribution.
69
Standardized Distribution
A distribution of scores that has been transformed to create pre-determined values for the mean and standard deviation.
70
Statistic
A value, usually numerical, that describes a sample (e.g., M, SD).
71
Stratified Random Sampling
A probability sampling method where the population is divided into subgroups (strata) and random samples are taken from each stratum.
72
Symmetrical Distribution
A distribution in which the right side is a mirror image of the left side.
73
Systematic Sampling
A probability sampling method where every nth participant is selected from a list.
74
Testing
A threat to internal validity where the act of taking a test affects performance on subsequent tests.
75
Test-retest reliability
A measure of reliability obtained by comparing the scores of the same individuals on the same measure at two different times.
76
Validity
The degree to which a measurement process measures the variable it claims to measure.
77
Weighted Mean
The overall mean calculated from two or more separate sets of scores, accounting for the different sample sizes of each set.
78
Within-Subject Design
An experimental design where the same group of participants is tested in all conditions.
79
Z-score
A standardized score that specifies the precise location of each raw score in a distribution, indicating the number of standard deviation units a score is from the mean.
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