Midterm 1 Flashcards

1
Q

Independent samples t-test

A

The scores in one sample do not tell us anything about the scores in the other sample (e.g., drawing names from two different hats)

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2
Q

Paired (dependent) samples t-test

A

Each score in one sample is connected to/paired with/dependent on a score in the other sample (e.g., sample 1: parents, sample 2: their infants; sample 1: pre-test, sample 2: post-test)

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3
Q

ANOVA

A

Testing hypotheses about multiple population means. Using independent samples (e.g., group A - pill A, group B - pill B, group C - pill C)

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4
Q

Sum of squares between

A

Sum of squares between group means and the grand mean. As the name suggests, it quantifies the variability between the groups of interest. Variability of group means around overall means.

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5
Q

Degrees of freedom

A

Number of values in the final calculations that are free to vary

df = n-1

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6
Q

Sum of square error

A

Sum of squares between the data and the group means. It quantifies the variability within the groups of interest.

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7
Q

Univariate

A

Univariate analysis is the simplest of the three analyses where the data you are analyzing is only one variable.

The most common univariate analysis is checking the central tendency (mean, median and mode), the range, the maximum and minimum values, and standard deviation of a variable.

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8
Q

multivariate analysis

A

looks at more than 2 variables and their relationship.

create a 3-d model to study the relationship

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9
Q

principles of science: determinism

A

consider how specific events or contexts predict certain behaviors, how behaviors might influence other behaviors, how cognitions can be influenced by contexts or behaviors, etc.

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10
Q

parsimony

A

Attempts to develop explanations that can be easily understood, but also minimize complexity where complication is unnecessary. In other words – don’t include unnecessary variables; don’t leave out necessary variables

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11
Q

Testability

A

Primary task of science, to try to falsify ideas, i.e. to stringently test our theories
Scientific tests are articulated in the form of hypotheses; and the form of a hypothesis should serve to suggest an appropriate statistical test

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12
Q

Replicability

A

The purpose in publishing an investigation’s results is not just to provide a context for replication and contribute to the scientific body of knowledge; it is to provide an opportunity for other interested scholars of science to examine an investigation’s procedures and confirm that they have no objections to the manner in which the evidence was collected and evaluated.

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13
Q

Empiricism

A

Scientists seek to identify ways to obtain recordable observations of events, objects, and/or concepts that comprise a theory under consideration; a researcher must express these “ways” as operational definitions

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14
Q

Instantiation

A

A deliberate process that involves specifying concrete instances of abstract concepts in order to help clarify their meaning

Crucial process for refining initial theoretical ideas
All theories must be subjected to instantiation process, either to make the definition of “fuzzy” concepts clearer and more communicable

E.g., Attitudes towards college influences whether or not a person goes to college” → high school seniors only, or juniors?

E.g., When assessing attitudes, should one make distinction between attitudes towards community colleges, 4-year colleges, and universities? What about trade schools?

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15
Q

proximal and distal

A

Proximal determinants are the more immediate determinants of behavior, and distal determinants are variables that influence behavior but do so through the more immediate determinants

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16
Q

etic and emic

A

The essence of emic approaches is to understand a culture in the way that members of that culture understand it, to learn the concepts they use, and to try to see the world as they do.

By contrast, the essence of etic approaches is to understand a culture in more abstract scientific terms that apply across different cultures and that can be used to make cross-cultural comparisons.

An emic construct is one that reflects the perceptions and understandings of the members of a culture. For example, the concept of “motherhood” would be defined emically by how members of a cultural group view that role.

Etic constructs, by contrast, focus is not on how a specific culture defines “motherhood” but rather on a conceptual definition of the concept of “motherhood” as used by the scientific community

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17
Q

Conceptual definition

A

Clear and concise definitions of one’s concepts

Process as “outlining constructs” or “stating what one means by the use of particular words”

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18
Q

Operational definition

A

In operationalizing “hunger”, psychologists have (1) asked people to respond to questionnaire items regarding their degree of perceived hunger; (2) deprived individuals of food for differing amounts of time so as to create more hunger in some than in others (e.g., people deprived for 24 hours must surely be hungrier than those deprived for only 2 hours); (3) measured the amount of food consumed from a standard portion given to each study participant (e.g., 2 pounds of spaghetti), under the assumption that the more a participant consumes (adjusted by body weight and metabolism), the hungrier he or she is; and (4) measured the amount of adversity the person will go through to obtain food.

All of the above seem to be reasonable procedures for measuring, that is, “operationalizing,” hunger. Unlike conceptual definitions, which often prove difficult to pin down, operational definitions are more concrete and thereby suggest a greater degree of precision and rigor.

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19
Q

Multidimensional constructs

A

Examples of multidimensional conceptualizations of constructs in the social sciences abound. For instance, theories of risk taking have delineated four types of risk taking propensities of individuals: (1) physical risk taking (putting oneself in harm’s way, physically), (2) social risk taking (taking risks in social relationships), (3) monetary risk taking (taking risks with money), and (4) moral risk taking (taking risks involving the breaking of rules or laws). These “components” of risk taking derive from the proposition that risk taking can occur in different settings and that risk tendencies can vary as a function of setting

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20
Q

shared vs surplus meanings

A

Given that each investigator makes his or her conceptual definition explicit (by providing clearly articulated and precisely defined propositions), specific points of agreement and disagreement can be identified. The points of agreement may then be assumed to represent the essential (i.e., agreed-upon) core of the concept. Researchers refer to this as shared meaning, which can be contrasted with the remainder, which is termed surplus meaning. In science, it is better to have concepts that are dominated by shared meaning and that do not have too much surplus meaning.

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21
Q

slope

A

in regression analysis, the slope represents the direction and strength of the relationship between a predictor and criterion variable. It indicates how much the criterion variable is expected to change as the predictor variable increases by one unit.

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22
Q

y-intercept

A

The y-intercept is the point where the regression line crosses the y-axis, representing the value of the criterion variable when all predictors are equal to zero.

Example: In predicting academic performance (criterion) based on study hours (predictor), the y-intercept would show the expected score when no time is spent studying.

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23
Q

predictor

A

Definition: A predictor variable (independent variable) is a variable that is used to predict or explain changes in the criterion variable. It is the “cause” in a non-experimental relationship.

Example: In predicting mental health (criterion) from social support (predictor), social support would be the predictor variable.

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24
Q

criterion

A

The criterion variable (dependent variable) is the outcome or effect that is predicted by the predictor variable in a non-experimental study.

Example: In a study examining stress (criterion) based on sleep quality (predictor), stress would be the criterion variable.

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25
Beta coefficient
The beta coefficient in non-experimental psychology quantifies the strength and direction of the relationship between a predictor and the criterion. It indicates how much change in the criterion variable is expected for each unit change in the predictor. Example: A beta coefficient of 0.5 for a predictor means a 1-unit increase in the predictor is associated with a 0.5-unit increase in the criterion.
26
Asymmetric influence
Asymmetric influence refers to a situation where one variable has a stronger or different effect on the other. For example, the impact of the predictor on the criterion is not the same in all directions. Example: A stronger influence of stress on anxiety than anxiety on stress would be an example of asymmetric influence.
27
Symmetric influence
Symmetric influence suggests that the relationship between variables is bidirectional or similar in both directions. Both variables influence each other in a comparable way. Example: In a study of social support and mental health, if both social support affects mental health and mental health affects social support equally, this would be symmetric influence.
28
Linear equation
Definition: A linear equation in non-experimental psychology is used to model the relationship between variables, where the change in one variable is constant relative to changes in the other variable. Example: In predicting anxiety (criterion) based on hours of sleep (predictor), the relationship may be modeled by a linear equation: Anxiety = 2(Sleep) + 4.
29
Functional relationship
*refers to whether the actual relationship between two variables is linear (where the linear regression line predicts the relationship well) or some other type of curvilinear relationship (a single curve, like a u-shaped prediction line), or two or three curves. The term "functional relationship" regards whether the relationship is linear or curvilinear.
30
Root Mean Square Error (of estimation)
The root mean square error (RMSE) is a measure of the difference between the observed and predicted values of a criterion. It gives an indication of how well a model fits the data. Lower RMSE means better model accuracy. Example: In predicting test scores, RMSE shows the average difference between the actual scores and the predicted scores.
31
Standard error of the estimate
Definition: The standard error of the estimate measures the accuracy of predictions made by a regression model. It is the average distance between the observed values and the predicted values. Example: A small standard error of the estimate indicates that the predicted values are close to the observed values.
32
Orthogonal factors (constructs)
Definition: Orthogonal factors are constructs or variables that are statistically independent of each other. In non-experimental psychology, this means changes in one factor do not influence changes in the other. Example: In a study of personality, extraversion and openness might be orthogonal factors if they are not related to each other.
33
Oblique factors (constructs)
Definition: Oblique factors are constructs that are correlated with each other. In non-experimental psychology, oblique factors are variables where changes in one factor are related to changes in another. Example: In personality research, extraversion and agreeableness may be oblique factors because they often share some level of correlation.
34
R² (R-squared)
Definition: R² is a statistic that indicates how well the predictor variables explain the variation in the criterion variable. It ranges from 0 to 1, with higher values indicating a better fit. Example: An R² of 0.75 suggests that 75% of the variation in the criterion can be explained by the predictors.
35
Triangle-R²
Triangle-R² is a measure used in factor analysis to assess how well the factors in the model explain the variance in the observed data. It is an extension of R², focusing on the contribution of factors rather than individual predictors. Example: If triangle-R² is high, it suggests that the factors in the model effectively capture the underlying structure of the data.
36
latent vs manifest variables
*manifest variables (aka manifest constructs) are variables that can be directly measured. for example: how long does it take one to do something, like their reaction time to a bell ringing - or - another type of amount: the number of times they call their mom every week Latent variables (aka latent constructs) are variables that cannot be directly measured. These conceptual ideas that need to be operationally defined by the use of manifest variables to measure that for which they are truly trying to represent. for example: "love" is a latent variable that might be measured in a questionnaire by asking how safe one feels with their intimate other, or how compassionate they feel toward them, or how many times a day they think about them
37
construct
38
heuristic
39
creativity
40
"the will to create"
41
unstructured interview
42
semi-structured interview
43
structured interview
44
symbolic interactionism
45
active interview
*Active interview styles focus on securing the interviewee's trust to generate meaning-making moments
46
dramaturgical interview
*Dramaturgical styles suggest that interviewers and interviewees join the interview event with preconceived ideas about the interview -- this includes expectations about the "roles" that they will be playing, but also preconceptions about what questions and responses should be like and where the emphases should be
47
role-biasing effects
48
full-channel listening
*is the term that refers to the interviewer paying attention to all the interviewees behaviors during the interview...the content of their responses of course, but also, the rapidity of their responses, changes in facial expressions, reluctance to discuss certain topics, use of hands, changes in posture, etc.
49
question schedule/throw-away questions
50
alternate (validating) questions
51
probe questions
52
zero-order level of communication
53
focus group
54
qualitative interview data
55
transcript vs. abridged transcript vs. note based vs. memory based
56
positive vs negative questions
57
classic content analysis
58
analytic framework
59
concept comparitive vs. individual change vs. critical influences vs. key concepts vs. choice
60
experimental research design
*seeks to