1
Q
What is the purpose of qualitative research?
A
Explore meaning, experience, & context
2
Q
What data type is used in qualitative research?
A
Non-numerical (e.g. interviews, narratives)
3
Q
What is the primary approach of quantitative research?
A
Deductive, confirmatory
4
Q
What type of analysis is used in qualitative research?
A
Thematic, content, grounded theory
5
Q
What outcome is typically sought in quantitative research?
A
Generalizable findings, statistical significance
6
Q
What is an example of qualitative research?
A
In-depth interviews with individuals diagnosed with Major Depressive Disorder
7
Q
What is an example of quantitative research?
A
Comparing Beck Depression Inventory scores across treatment groups using ANOVA
8
Q
What is the first basic step in planning experimental research?
A
Formulate a Research Question or Hypothesis
9
Q
What is an independent variable (IV)?
A
Manipulated factor (e.g., type of therapy)
10
Q
What is a dependent variable (DV)?
A
Measured outcome (e.g., BDI-II scores)
11
Q
What defines a true experimental research design?
A
Random assignment to conditions
12
Q
What is a quasi-experimental research design?
A
Lacks random assignment but involves manipulation of IV
13
Q
What is random assignment used for in experimental research?
A
To reduce bias and ensure internal validity
14
Q
What is the difference between random selection and random assignment?
A
Random assignment enhances causal inference; random selection enhances generalizability
15
Q
What is probability sampling?
A
Methods ensuring each member of the population has a known chance of selection
16
Q
What is simple random sampling?
A
Every individual has an equal chance of being selected
17
Q
What is stratified random sampling?
A
Population divided into subgroups, then randomly sampled within each
18
Q
What is cluster sampling?
A
Randomly selecting entire groups or clusters for study
19
Q
What is a mediator variable?
A
Explains how or why an IV affects a DV
20
Q
What is a moderator variable?
A
Affects the strength or direction of the relationship between IV and DV
21
Q
What is the role of extraneous variables in research?
A
Factors not intentionally studied that may influence the DV
22
Q
What is random assignment’s role in controlling extraneous variables?
A
Distributes extraneous variables evenly across treatment groups
23
Q
What technique involves keeping an extraneous variable constant?
A
Holding the Extraneous Variable Constant
24
Q
What is matching subjects in research?
A
Pairing participants with similar scores on an extraneous variable
25
What does statistical control involve?
Using statistical techniques to adjust for the influence of extraneous variables
26
What is the definition of random error?
Unpredictable fluctuations in measurement that occur by chance
27
Fill in the blank: The _______ variable is presumed cause or predictor.
Independent
28
Fill in the blank: The _______ variable is presumed effect or response.
Dependent
29
True or False: Qualitative research aims to test a hypothesis.
False
30
True or False: In true experimental research, random assignment ensures groups are equivalent at baseline.
True
31
What is the goal of qualitative research?
To explore lived experiences, perceptions, or social phenomena
32
What is the definition of Blocking in research?
Treat the extraneous variable as a second IV to examine its influence.
## Footnote
Example: Include therapist experience level as a block in therapy outcome studies.
33
What does Statistical Control refer to?
Use statistical techniques to adjust for the influence of extraneous variables.
## Footnote
Example: Use ANCOVA to control for baseline depression scores.
34
What is Random Error?
Unpredictable fluctuations in measurement that occur by chance.
## Footnote
It does not consistently bias results but can reduce reliability.
35
What are techniques to minimize Random Error?
* Standardized Procedures
* Reliable Instruments
* Training of Assessors
* Multiple Measurements
36
What is Internal Validity?
A study's ability to accurately show whether the independent variable affects the dependent variable.
37
What is External Validity?
The extent to which findings can be generalized beyond the original context.
38
What is the Maturation threat to internal validity?
Natural changes in participants over time, such as aging or fatigue.
39
What is the History threat to internal validity?
External events occurring during the study that affect outcomes.
40
What does Testing refer to as a threat to internal validity?
Repeated testing influences performance, leading to practice or sensitization effects.
41
What is Instrumentation in the context of internal validity threats?
Changes in measurement tools or procedures over time.
42
What is Statistical Regression as a threat to internal validity?
Extreme scores tend to move toward the mean on retesting.
43
What does Selection refer to in internal validity threats?
Systematic differences between groups at baseline, leading to assignment issues.
44
What is Attrition (Morality) as a threat to internal validity?
Differential dropout rates across groups.
45
What are the Interaction threats to internal validity?
Threats like history or maturation affect groups differently.
46
What is the Interaction Between Testing and Treatment threat to external validity?
Pretesting alters participants’ responsiveness to the treatment.
47
What is the Interaction Between Selection and Treatment threat to external validity?
The treatment effect may differ across populations due to selection characteristics.
48
What are Reactive Arrangements (Reactivity)?
Participants alter their behavior because they know they are being observed.
49
What are some phenomena included in reactivity?
* Evaluation Apprehension
* Demand Characteristics
* Experimenter Expectancy
50
What is Multiple Treatment Interference?
Exposure to multiple treatments affects outcomes, complicating isolation of effects.
51
What is a Between-Group Design?
Participants are assigned to different conditions or groups, each receiving a unique level of the IV.
52
What is a Factorial Design?
Involves two or more IVs, each with multiple levels, allowing analysis of main effects and interactions.
53
What is a Main Effect in research design?
The independent influence of one variable on the dependent variable, ignoring other variables.
54
How is an Interaction Effect defined?
When the effect of one IV on the DV depends on the level of another IV.
55
What is a Within-Subject Design?
Same participants are exposed to all levels of the IV.
56
What are the advantages of Within-Subject Design?
* Controls for individual differences
* Requires fewer participants
* Increased statistical power
57
What are the disadvantages of Within-Subject Design?
* Autocorrelation
* Type I error inflation
* Order/carryover effects
58
What is a Mixed Design in research?
Combines elements of both between-group and within-subject designs.
59
What are Single-Subject Designs characterized by?
At least two phases: a baseline (no treatment) phase and a treatment phase.
60
What is an AB Design?
Tracks behavior across two distinct phases: baseline and intervention.
61
What is a Reversal Design?
Systematically introduces and withdraws IV to determine causation.
62
What is a Multiple Baseline Design?
Apply the same intervention across multiple subjects, behaviors, or settings without withdrawing it.
63
What is the purpose of Descriptive Statistics?
Summarize and organize data.
64
What is the purpose of Inferential Statistics?
Make predictions or generalizations about a population based on sample data.
65
What is a Nominal scale of measurement?
Categories with no inherent order; least mathematically complex.
66
What is an Ordinal scale?
Ranked order with unequal intervals.
67
What is an Interval scale?
Equal intervals with no true zero.
68
What are the three types of measurement scales?
Nominal, Ordinal, Interval, Ratio
## Footnote
Nominal: categories without order; Ordinal: ranked order; Interval: equal intervals without true zero; Ratio: equal intervals with true zero.
69
What is the definition of an ordinal scale?
Ranked order, unequal intervals
## Footnote
Allows comparison of more or less of a characteristic.
70
What statistical measures are commonly used with ordinal data?
Median, percentiles, Likert Scale
## Footnote
Likert scales often treated as interval or ratio.
71
Define interval scale.
Equal intervals, no true zero
## Footnote
Allows for mathematical operations like mean and standard deviation.
72
What is a key characteristic of a ratio scale?
Equal intervals with true zero
## Footnote
Allows for all mathematical operations, including multiplication and division.
73
What are the key types of descriptive statistics?
Measures of Central Tendency, Measures of Dispersion, Distribution Shape
## Footnote
Includes mean, median, mode, range, variance, and standard deviation.
74
What does a frequency polygon represent?
A line graph showing the distribution of scores
## Footnote
Resembles a bell-shaped curve when data is normally distributed.
75
What does kurtosis measure?
How peaked is the curve?
## Footnote
Types include leptokurtic (very peaked), platykurtic (flat), and mesokurtic (normal).
76
What is a positively skewed distribution?
Mean > Median > Mode
## Footnote
Most scores are on the low side with a long tail on the high side.
77
What indicates a negatively skewed distribution?
Mean < Median < Mode
## Footnote
Most scores are on the high side with a long tail on the low side.
78
What is the mode in statistics?
The value that occurs most often
## Footnote
Can be used with nominal data and distribution can be multimodal.
79
How is the median defined?
The middle score when values are ordered
## Footnote
Resistant to outliers and used when data is skewed.
80
What is the arithmetic mean?
Sum of all values divided by the number of values
## Footnote
Sensitive to outliers and best used with interval or ratio data.
81
What does variability measure?
How spread out scores are in a distribution
## Footnote
Key measures include range, variance, and standard deviation.
82
What is the formula for variance?
Average of squared deviations from the mean
## Footnote
It emphasizes larger deviations and is used in inferential statistics.
83
What does standard deviation quantify?
The average distance of each score from the mean
## Footnote
Expressed in the same units as the original data.
84
How does sample size affect the Standard Error of the Mean (SEM)?
Larger sample sizes produce smaller SEM
## Footnote
This leads to more stable and precise estimates of the population mean.
85
What is the Central Limit Theorem (CLT)?
The sampling distribution of the mean will be approximately normal
## Footnote
Even if the population distribution is skewed.
86
What is a population parameter?
A value that describes a characteristic of an entire population
## Footnote
Example: True mean IQ of all adults in the U.S.
87
What is a sample statistic?
A value calculated from a subset of the population
## Footnote
Example: Mean IQ from a sample of 200 adults.
88
What is the relationship between sample statistics and population parameters?
Sample statistics estimate population parameters
## Footnote
Studying the entire population is rarely feasible.
89
What does the formula for Standard Error of the Mean (SEM) state?
SEM = σ / √n
## Footnote
Reflects how much sample means are expected to vary from the population mean.
90
True or False: The mean, median, and mode are equal in a normal distribution.
True
## Footnote
In a perfectly normal distribution, they are all the same.
91
What happens to the measures of central tendency when a constant is added to each score?
They shift by the constant
## Footnote
The shape of the distribution remains the same.
92
What is the effect of multiplying or dividing scores by a constant?
Mean, median, mode are scaled and range, variance, standard deviation also change
## Footnote
Variance changes by the square of the constant.
93
What is the formula for calculating the Standard Error of the Mean (SEM)?
SEM = standard deviation / √sample size
94
How does an increase in sample size affect SEM?
As sample size increases, SEM decreases
95
What does a smaller SEM indicate about the population mean estimate?
It makes the estimate of the population mean more reliable
96
What are the implications of understanding sampling distributions in DSM-5-TR?
* Prevalence estimates of disorders
* Treatment efficacy across clinical trials
* Norm-referenced scores (e.g., IQ, symptom severity)
97
What do confidence intervals indicate in hypothesis testing?
Narrower intervals with larger samples
98
What is the purpose of hypothesis testing?
To determine whether observed data supports a specific prediction about a population
99
Define Null Hypothesis (H₀)
Assumes no effect, no difference, or no relationship
100
Give an example of a Null Hypothesis (H₀)
CBT has no effect on depression severity
101
Define Alternative Hypothesis (H₁)
Predicts an effect, difference, or relationship
102
Give an example of an Alternative Hypothesis (H₁)
CBT reduces depression severity
103
What is a Nondirectional (Two-Tailed) Hypothesis?
Predicts a difference but not the direction
104
Provide an example of a Nondirectional Hypothesis.
CBT affects depression severity (could increase or decrease)
105
What is a Directional (One-Tailed) Hypothesis?
Predicts a specific direction of effect
106
Provide an example of a Directional Hypothesis.
CBT will reduce depression severity
107
What is the Retention Region in hypothesis testing?
The range where the test statistic suggests the data is consistent with H₀
108
What does it mean when results are in the Rejection Region?
H₀ is rejected and H₁ is retained
109
What is an Alpha Level (α)?
The probability of making a Type I error
110
What is a common Alpha Level (α) used in research?
0.05
111
What does a p-value represent?
Probability of obtaining observed results if H₀ is true
112
What indicates that we should reject H₀?
If p-value < α
113
What is the difference between a One-Tailed and Two-Tailed Test?
One-Tailed has entire α in one direction; Two-Tailed splits α between both tails
114
What is a Type I Error?
Rejecting a true null hypothesis
115
Provide an example of a Type I Error.
Concluding that a new intervention reduces PTSD symptoms when it does not
116
What is a Type II Error?
Retaining a false null hypothesis
117
Provide an example of a Type II Error.
Failing to detect that a culturally adapted depression scale is more valid
118
What does it mean to have statistical power in hypothesis testing?
The probability of correctly rejecting a false null hypothesis
119
What strategies can be used to increase statistical power?
* Increase alpha
* Increase sample size
* Increase effects of the IV
* Minimize error variance
* Use one-tailed test when justified
* Use parametric tests
120
What are Parametric Tests used for?
When data is interval or ratio scale and assumptions of normality and homogeneity are met
121
What are Nonparametric Tests used for?
When data does not meet parametric assumptions or is nominal/ordinal scale
122
What is the formula for degrees of freedom (df) in a t-Test for Independent Samples?
df = n1 + n2 - 2
123
What does the Critical Value represent?
The cutoff point beyond which you reject the null hypothesis
124
What factors determine the Critical Value?
* Alpha level (α)
* Degrees of freedom (df)
125
What is the purpose of the Mann-Whitney U Test?
To compare two independent groups on an ordinal or non-normally distributed continuous variable
126
What is the statistic used in a Single-Sample Chi-Square Test?
X²
127
What are the degrees of freedom for a Multiple-Sample Chi-Square Test?
df = (C-1)(R-1)
128
What is the purpose of using a Kruskal-Wallis test?
To analyze two or more independent groups on an ordinal variable
129
What is a Mixed ANOVA?
An analysis that includes both independent and correlated groups
130
What statistical test is used to analyze data with one independent variable and two independent groups on an ordinal or non-normally distributed continuous variable?
Wilcoxon Rank-Sum Test (U)
## Footnote
Nonparametric alternative to the independent samples t-test.
131
What is the purpose of the Wilcoxon Rank-Sum Test?
Compares two independent (unrelated) groups/samples on an ordinal or non-normally distributed continuous variable.
132
What type of data does the Wilcoxon Matched-Pairs Signed-Ranks Test analyze?
Difference of scores on the dependent variable for pairs of subjects converted to ranks.
133
What is the purpose of the Wilcoxon Matched-Pairs Signed-Ranks Test?
Compares two related samples or repeated measures on an ordinal variable.
134
What statistical test is used to compare three or more independent groups on ranked data?
Kruskal-Wallis Test (H)
## Footnote
Nonparametric alternative to one-way ANOVA.
135
What is the main advantage of using ANOVA over multiple t-tests?
Controls for Type I error better than multiple t-tests.
136
What is the degrees of freedom formula for a Student’s t-test for independent samples?
df = n_1 + n_2 - 2
137
What is the purpose of the Analysis of Variance (ANOVA)?
Tests whether the means of two or more groups differ significantly.
138
What is the F statistic in ANOVA?
F = MSB/MSW
## Footnote
MSB = mean square between; MSW = mean square within.
139
What does a significant F statistic indicate in ANOVA?
At least one group differs in median ranks.
140
What is the logic behind Factorial ANOVA?
Partitions variability between groups to obtain F-ratios for main effects and interactions.
141
What is the purpose of Analysis of Covariance (ANCOVA)?
Combines ANOVA and regression analysis to control for continuous covariates.
142
What does the term 'Experimentwise Error Rate' refer to?
Cumulative Type I error across multiple comparisons.
143
What does Cohen’s d measure?
Standardized difference between two means.
144
Interpret a Cohen’s d value of 0.6.
Indicates a moderate effect.
145
What is the purpose of correlation techniques?
Determine the degree of association between two or more variables.
146
What do correlation coefficients indicate?
The average degree of association between variables.
147
What is the range of correlation coefficients?
−1.0 to +1.0
148
What is the interpretation of a Pearson r of -0.70?
Strong inverse relationship between variables.
149
What assumption is made in correlation and regression analysis regarding the relationship between variables?
The relationship is linear.
150
What is the purpose of multiple regression analysis?
Predict one dependent variable from multiple independent variables.
151
What is multicollinearity?
High correlations among predictors in multiple regression.
152
What is the goal of stepwise regression?
Explain the greatest amount of variability in the criterion using the fewest number of predictors.
153
What is the primary difference between multiple regression and ANOVA?
Multiple regression predicts a continuous DV from continuous/categorical IVs.
154
Fill in the blank: The __________ is the proportion of shared variance between X and Y.
Coefficient of Determination (r²)
155
What is the Standard (Simultaneous) method in predicting GPA?
All predictors entered at once
156
What is the Hierarchical method in predicting GPA?
Predictors entered in blocks based on theory
157
In the Hierarchical method, which predictor is entered first?
SES
158
What does Multiple Regression predict?
Predicts continuous DV from continuous/categorical IVs
159
When is Multiple Regression often used instead of ANOVA?
When groups are unequal in size
160
What is a key advantage of Multiple Regression?
Permits adding or subtracting independent variables to determine the best subset
161
What does ANOVA compare?
Compares group means with categorical IVs only
162
What is Cross-Validation in the context of multiple regression?
Testing the regression equation on another sample to assess generalizability
163
What happens to the multiple correlation coefficient during cross-validation?
It tends to shrink
164
What is Canonical Correlation used for?
Examines relationships between two sets of variables
165
What type of predictors does Canonical Correlation use?
Continuous predictors to predict continuous criteria
166
What is Discriminant Function Analysis used for?
Classifying individuals into predefined categories using several predictors
167
What type of relationships does Discriminant Function Analysis assume?
Linear relationships
168
What is Logistic Regression used for?
Classifying individuals into predefined categories using nonlinear relationships
169
What is the purpose of Causal Modeling?
To test a causal model or theory about relationships among variables
170
What does Path Analysis test?
Direct and indirect relationships among observed variables
171
How does Structural Equation Modeling (SEM) differ from Path Analysis?
SEM combines path analysis and factor analysis
172
What type of variables does SEM provide information about?
Both observed variables and latent traits
173
What is Factor Analysis used for?
Identifying underlying dimensions from correlated variables
174
What does Cluster Analysis do?
Groups individuals into mutually exclusive and exhaustive subgroups based on similarity
175
Fill in the blank: Multiple Regression is useful when groups are ______ in size.
unequal
176
True or False: ANOVA can be used with continuous independent variables.
False
177
Fill in the blank: Causal Modeling techniques include Path Analysis and ______.
Structural Equation Modeling (SEM)
178
What is an example of Discriminant Function Analysis?
Predicting therapy outcome from coping style and symptom severity
179
What is an example of Logistic Regression?
Predicting suicide risk from depression and substance use
180
What does Factor Analysis reduce?
It reduces multiple correlated variables into fewer factors
181
What is an example of Factor Analysis?
Reducing 20 anxiety items into 3 factors
182
What is an example of Cluster Analysis?
Segmenting clients into therapy-responsive vs. resistant clusters
EPPP
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