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Advanced research methods UNIsa > week 3 Factorial anova > Flashcards

week 3 Factorial anova Flashcards

1
Q

What is a ‘factor’ in the context of factorial designs?

A

A ‘factor’ is an independent variable (IV).

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2
Q

What does ‘factorial’ mean in research design?

A

Factorial’ refers to the use of two or more independent variables (IVs).

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3
Q

A ____ represents the relationship between variables of interest, such as the correlation calculated in a study.

A

Model

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4
Q

An ____ variable (IV) is a factor that is manipulated in an experiment to observe its effect on a dependent variable (DV).

A

Independent

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5
Q

How does complexity in factorial designs affect the interpretation of data?

A

Complexity shows that the relationship between independent variables (IVs) and the dependent variable (DV) can be conditional, meaning the effect of an IV on a DV can depend on the level/status of another IV.

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6
Q

What statistical test is used for one IV with 2 levels?

A

A t-test is used for evaluating the effect of one IV with two levels.

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7
Q

A ________ is used when evaluating the effect of one independent variable with more than two levels.

A

one-way ANOVA

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8
Q

What does a Two-way ANOVA signify?

A

A Two-way ANOVA involves the examination of two independent variables.

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9
Q

What is the purpose of measuring DV before and after interventions in factorial designs?

A

Measuring DV before and after interventions helps assess the effect of the independent variables on the dependent variable over time.

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10
Q

A ___ is the outcome that researchers measure to assess the effect of one or more independent variables.

A

dependent variable (DV)

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11
Q

Define a ‘3 by 2 ANOVA’ in the context of factorial designs.

A

A ‘3 by 2 ANOVA’ refers to a factorial ANOVA with three levels of one IV and two levels of another IV.

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12
Q

What is the significance of an architect’s model in research analogy?

A

An architect’s model symbolizes that real-world representations (like models used in research) will always have some degree of error and cannot be perfectly accurate.

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13
Q

What is the main effect in factorial designs?

A

The main effect is the effect of an independent variable (IV) on a dependent variable (DV), averaging out the levels of all other IVs.

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14
Q

The effect of an independent variable (IV) on a dependent variable (DV), averaged across the levels of other IVs.

A

Main Effect

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15
Q

How do interaction effects differ from main effects?

A

Interaction effects occur when the relationship between each IV and the DV varies depending on the value of the other IV, while main effects consider each IV in isolation

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16
Q

An effect that occurs when the relationship between an independent variable (IV) and a dependent variable (DV) depends on the level of another IV.

A

Interaction Effect

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17
Q

How does a factorial design increase statistical power?

A

A factorial design increases power by removing variance explained by other variables and interactions from error.

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18
Q

The probability of correctly rejecting the null hypothesis when it is false, often enhanced in factorial designs by controlling for additional variance.

A

Statistical Power

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19
Q

What are the two types of variables examined in the context of interaction effects?

A

The two types of variables are independent variables (IVs) and the dependent variable (DV).

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20
Q

The variable being measured or tested in an experiment, which is expected to change due to the manipulation of the independent variables (IVs).

A

Dependent Variable (DV)

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21
Q

An experimental design that examines the effects of two or more independent variables (IVs) simultaneously, including their interaction effects.

A

Factorial Design

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22
Q

Why might interaction effects be considered more interesting than main effects?

A

Interaction effects show how the effects of one IV might depend on the levels of another, often revealing complex relationships that are more relevant to research hypotheses

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23
Q

A statement predicting the relationship between variables, which may be tested through empirical research and observed in interaction effects.

A

Research Hypothesis

24
Q

What visual aids can help interpret interaction effects in research?

A

Graphs or plots that depict the interaction between IVs and their effects on the DV can dramatically clarify how these relationships function.

25
Q

A statistical test used to determine the effect of two independent variables on a dependent variable, including their interaction effects.

A

Two-Way ANOVA

26
Q

What is the significance of collapsing categories in factorial designs?

A

Collapsing categories allows researchers to examine main effects by disregarding one IV while focusing on another, simplifying analysis but potentially overlooking interaction effects.

27
Q

What is ANCOVA?

A

ANCOVA, or Analysis of Covariance, is an ANOVA that adjusts for the effect of an extraneous variable on the dependent variable (DV)

28
Q

How does ANCOVA reduce error variance?

A

ANCOVA reduces error variance by explaining some of the unexplained variance through the inclusion of covariates, which improves the reliability of the model

29
Q

A ______ is an extraneous variable that is not of primary interest but can affect the dependent variable, leading to reduced accuracy in the analysis.

A

nuisance variable

30
Q

What impact does including covariates have on the F-ratio in ANCOVA?

A

Including covariates reduces the residual variance, which improves the F-ratio and increases the likelihood of detecting an effect.

31
Q

A ___is a quantitative variable that is added to a statistical model in ANCOVA to control for its potential influence on the dependent variable.

A

covariate

32
Q

What does SST stand for in the context of ANCOVA?

A

SST stands for Total Variance in the Data.

33
Q

___ represents the Improvement Due to the Model, indicating the variance explained by the model in ANCOVA.

A

SSM

34
Q

What does SSR refer to in ANCOVA?

A

SSR refers to Error in the Model, indicating the residual variance that remains unexplained after accounting for the covariates.

35
Q

______in ANCOVA refers to the ability to minimize the influence of extraneous variables, leading to clearer insights into the effects of predictors.

A

Experimental Control

36
Q

What are the basic assumptions for ANCOVA?

A

The basic assumptions for ANCOVA include that the covariate must be related to the DV and that there is linearity between the covariate and the DV.

37
Q

______ is the portion of variance in the dependent variable that is not explained by the model or covariates.

A

Residual variance

38
Q

How could you test for linearity between the covariate and the dependent variable?

A

You can test for linearity by creating a scatterplot of the dependent variable and the covariate to visually assess the linear relationship.

39
Q

The _____ is a statistic used to determine the ratio of variance explained by the model to the unexplained variance, helping to assess the significance of the model.

A

F-ratio

40
Q

Why might the means in a study be affected by age or gender?

A

Differences in means might be affected by age or gender, which are variables that can influence the dependent variable, hence necessitating their inclusion as covariates in ANCOVA.

41
Q

____ is the unexplained variance in a statistical model, which can obscure the true effect of the independent variable on the dependent variable.

A

Error variance

42
Q

What does ANOVA stand for?

A

ANOVA stands for Analysis of Variance.

43
Q

What is the primary purpose of ANOVA?

A

The primary purpose of ANOVA is to compare means among three or more groups to determine if at least one group mean is statistically different from the others.

44
Q

What does ANCOVA stand for?

A

ANCOVA stands for Analysis of Covariance, which is used to compare one or more means while controlling for the effects of one or more covariate variables.

45
Q

What are between-groups designs?

A

Between-groups designs involve different participants being assigned to each group, allowing for the comparison of means across distinct groups.

46
Q

____ designs are experimental designs where the same participants are subjected to all conditions, allowing for comparisons within the same group.

A

Within-groups

47
Q

What is a mixed ANOVA?

A

A mixed ANOVA is a statistical test that incorporates both between-groups and within-groups factors, allowing researchers to assess interactions between these types of variables.

48
Q

_____ is an experimental setup that tests the effects of two or more independent variables simultaneously, assessing both individual and interaction effects.

A

Factorial design

49
Q

What is an example of a mixed ANOVA design?

A

An example of a mixed ANOVA design is studying the effect of time (pre/post intervention, within subjects) and diet type (between subjects) on weight loss.

50
Q

A ___ is a variable that is not of primary interest but is controlled or accounted for in an analysis to reduce error and increase the precision of estimates.

A

covariate

51
Q

How does a mixed ANOVA address nuisance variables?

A

A mixed ANOVA controls for nuisance variables by incorporating covariates, allowing researchers to isolate the effects of the main independent variables of interest

52
Q

A ____ variable is an independent variable in an experiment where different participants experience different levels of that variable.

A

between-subjects

53
Q

In a mixed ANOVA, what does “pre/post” refer to?

A

In a mixed ANOVA, “pre/post” refers to measurements taken before and after an intervention, evaluating changes over time within the same subjects.

54
Q

A ____ is a statistical test used to compare the means of three or more unrelated groups based on one independent variable.

A

one-way ANOVA

55
Q

What distinguishes a factorial ANOVA from a one-way ANOVA?

A

A factorial ANOVA involves multiple independent variables allowing for the examination of their interactions, whereas a one-way ANOVA examines just one independent variable.

56
Q

An _____ occurs when the effect of one independent variable on the dependent variable changes depending on the level of another independent variable.

A

interaction effect

57
Q
A

Advanced research methods UNIsa (5 decks)

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