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Advanced research methods UNIsa > Week 2 - ANOVA theory > Flashcards

Week 2 - ANOVA theory Flashcards

1
Q

Anovo is short for what?

A

Analysis of variance

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2
Q

ANOVA Analysis is identical for ____ and non____ designs

A

experimental

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3
Q

why use ANOVA?

A

compare several means, can be used when you have manipulated two or more IVs

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4
Q

why not use multiple t-tests instead of ANOVA?

A

-Inflates the type 1 error rate (essentially the more tests we run, the more likely we are to get a false positive)
-can try controlling for it (bonferroni correction) but lose power.

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5
Q
  • Tests for an overall difference between groups.
    -Tells us that the group means are different but, doesn’t tell us exactly which means differ.
A

ANOVA

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6
Q

What does ANOVA stand for?

A

Analysis of Variance.

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7
Q

A statistical technique used to determine if there are any statistically significant differences between the means of three or more independent groups.

A

One-Way ANOVA

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8
Q

Who developed the basic technique of ANOVA?

A

Sir Ronald Fisher.

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9
Q

What is the primary focus of one-way ANOVA?

A

Investigating differences in means among multiple groups.

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10
Q

The hypothesis that states that there are no differences among the group means, implying that any observed differences are due to sampling error.

A

Null Hypothesis (H0)

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11
Q

The hypothesis that states at least one group mean is different from the others, suggesting significant effects among groups.

A

Alternative Hypothesis (H1)

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12
Q

What are the key assumptions of one-way ANOVA?

A

Normality, homogeneity of variance, and independence of observations.

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13
Q

The variation accounted for by the differences between the group means in an ANOVA.

A

Between-Group Variability

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14
Q

The variation accounted for by the differences within each group in an ANOVA.

A

Within-Group Variability

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15
Q

How is the F-ratio calculated in ANOVA?

A

The F-ratio is calculated by dividing the mean square between groups by the mean square within groups.

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16
Q

An estimate of variance calculated by dividing the sum of squares by the corresponding degrees of freedom.

A

Mean Square (MS)

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17
Q

What does a significant F-value indicate in ANOVA?

A

It suggests that there are significant differences among the group means, leading to the rejection of the null hypothesis.

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18
Q

A measure of the strength of the relationship between the independent variable and the dependent variable in ANOVA, commonly reported as eta squared (η²).

A

Effect Size

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19
Q

What is the purpose of post hoc tests in ANOVA?

A

To determine which specific group means are significantly different after finding a significant overall F-value.

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20
Q

A statistical adjustment method used to reduce the chances of Type I error when multiple comparisons are being made.

A

Bonferroni Correction

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21
Q

What is the role of the Levene’s test in ANOVA?

A

To check for homogeneity of variance across groups before proceeding with ANOVA.

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22
Q

A statistical test used to assess the normality of residuals in ANOVA.

A

Shapiro-Wilk Test

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23
Q

What is a repeated measures ANOVA?

A

An analysis that evaluates differences among means when the same subjects are used for each treatment.

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24
Q

The condition where variances of the differences between all combinations of related groups are equal, necessary for repeated measures ANOVA.

A

Sphericity

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25
Q

What is a primary limitation of ANOVA?

A

ANOVA does not indicate where differences lie among means, necessitating post hoc tests for further analysis.

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26
Q

A non-parametric alternative to one-way ANOVA, used when the assumption of normality is violated.

A

Kruskal-Wallis Test

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27
Q

What does the p-value represent in the context of an ANOVA test?

A

The probability of obtaining the observed results or more extreme results if the null hypothesis is true.

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28
Q

A measure of effect size that indicates the proportion of variance in the dependent variable that can be attributed to the independent variable.

A

Eta Squared (η²)

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29
Q

How can you check the assumptions of ANOVA visually?

A

By using QQ plots to assess normality and box plots to evaluate homogeneity of variances.

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30
Q

The differences between observed values and the values predicted by the ANOVA model, used to assess model fit and check assumptions.

A

Residuals

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31
Q

What are the main types of ANOVA

A

Between subjects, Repeated measures, Factorial, ANCOVA.

32
Q

A design where different levels of the independent variable (IV) are experienced by different entities, also known as “Independent ANOVA”.

A

Between-groups ANOVA

33
Q

What is ANCOVA?

A

ANCOVA stands for Analysis of Co-variance, and it can transform any of the three ANOVA types by adding covariates.

34
Q

An ANOVA design that includes at least one within-groups independent variable and one between groups independent variable, requiring at least two IVs.

A

Mixed ANOVA

35
Q

How does a one-way ANOVA differ from a factorial ANOVA?

A

A one-way ANOVA involves one independent variable, while a factorial ANOVA involves two or more independent variables.

36
Q

A design where different levels of the independent variable are experienced by the same entities over time, allowing for repeated measures.

A

Repeated measures ANOVA

37
Q

A type of ANOVA where different levels of the independent variable are experienced by different entities, also referred to as “Independent” ANOVA.

A

Between-subjects ANOVA

38
Q

What is a One-way ANOVA?

A

A One-way ANOVA examines the effect of one independent variable with different levels on a dependent variable.

39
Q

Repeated measures ANOVA

A

A design where different levels of the independent variable are experienced by the same entities, often utilized in studies with pre and post-intervention measurements.

40
Q

What constitutes a Factorial ANOVA?

A

A Factorial ANOVA involves two or more independent variables, with each participant experiencing one level of each independent variable.

41
Q

What is the purpose of a Mixed ANOVA design?

A

A Mixed ANOVA design includes both within-groups (repeated measures) and between-groups independent variables.

42
Q

A type of ANOVA where each subject experiences all levels of the independent variable, commonly referred to as “repeated measures” ANOVA.

A

Within-groups ANOVA

43
Q

How can any ANOVA type become an ANCOVA?

A

By adding one or more covariates to control for nuisance variables that may affect the dependent variable.

44
Q

A type of ANOVA that assesses the impact of a single independent variable on a dependent variable.

A

One-way ANOVA

45
Q

What is a factorial design in ANOVA?

A

A factorial design in ANOVA involves multiple independent variables and examines their combined effects on a dependent variable.

46
Q

An ANOVA design that includes at least one within-groups and one between-groups independent variable, used for more complex analyses

A

Mixed Factorial ANOVA

47
Q

Give an example of a Between-groups ANOVA scenario.

A

An example is studying how different dietary conditions impact weight loss by using separate groups of participants for each diet.

48
Q

A variable that is not the primary interest in a study but is controlled for in an analysis to reduce potential confounding.

A

Covariate

49
Q

What effect does adding covariates have in ANCOVA?

A

Adding covariates in ANCOVA helps control for variability and increase the precision of the results for the primary independent variables.

50
Q

A variable that may obscure or confound the results of an analysis if not controlled or accounted for.

A

Nuisance variable

51
Q

What does a Repeated measures ANOVA allow researchers to assess?

A

A Repeated measures ANOVA allows researchers to assess the impact of different interventions on the same subjects over time.

52
Q

The variable that is manipulated in an experiment to observe its effect on the dependent variable.

A

Independent Variable (IV)

53
Q

Why is classifying ANOVA types important in research?

A

Classifying ANOVA types is essential for clearly communicating the analysis strategy in research reports and ensuring appropriate statistical methods are used.

54
Q

The outcome variable that is measured to assess the effect of changes in the independent variable.

A

Dependent Variable (DV)

55
Q

What does “pre/post intervention” mean in the context of ANOVA?

A

“Pre/post intervention” refers to measurements taken before and after an intervention to observe changes attributable to the treatment.

56
Q

A document that presents and discusses the findings of a research study, including details about methodology, analysis, and conclusions.

A

Research Report

57
Q

What is the significance of the design in ANCOVA?

A

The design in ANCOVA significantly impacts how covariates are included and analyzed, which affects the interpretation of the results.

58
Q

The overarching plan or structure for conducting research that outlines how data will be collected and analyzed.

A

Experimental Design

59
Q

How does a Factorial ANOVA differ from a One-way ANOVA?

A

A Factorial ANOVA involves two or more independent variables, while a One-way ANOVA examines only one independent variable.

60
Q

What is the primary purpose of using within-groups (repeated measures) designs?

A

To measure each participant on the dependent variable (DV) at least twice to track changes over time or levels of the independent variable (IV).

61
Q

The assumption that the variances of the differences between all combinations of related groups (levels of the independent variable) are roughly equal.

A

Sphericity

62
Q

What are the advantages of using within-groups designs in research?

A

Each participant serves as their own control, which reduces the effect of individual differences and requires fewer participants for the same statistical power.

63
Q

What are two examples of when within-groups designs might be used?

A

Measuring aggression before and after treatment, or assessing weight at pre-diet, post-diet, and follow-up stages.

64
Q

A statistical test used to evaluate the assumption of sphericity in within-groups designs; if p < .05, the assumption is considered violated.

A

Mauchly’s Test

65
Q

What is one disadvantage of within-groups designs?

A

They are not possible for existing groups; for example, you cannot change dogs into cats for a study.

66
Q

An adjusted estimate used in statistical analysis when the sphericity assumption is violated, typically used when the estimate of sphericity is less than .75.

A

Greenhouse-Geisser Estimate

67
Q

How is sphericity tested in within-groups designs?

A

By assessing if the correlation between treatment levels is the same and using Mauchly’s test to verify if the assumption is violated.

68
Q

A more liberal adjustment used in statistical analysis when the sphericity assumption is violated, typically used when the sphericity estimate is greater than .75.

A

Huynh-Feldt Estimate

69
Q

What does a violation of the sphericity assumption indicate?

A

It suggests that the variances in the differences between conditions are not equal, affecting the validity of the statistical results.

70
Q

A test that assesses the homogeneity of variances between groups; used for between-groups designs rather than within-groups.

A

Levene’s Test

71
Q

Why is the assumption of sphericity often violated in large samples?

A

Because larger samples have more power to detect even small differences, making violations more likely.

72
Q

What is a practical implication of each participant being their own control in within-groups designs?

A

It minimizes variability due to individual differences, which can enhance the sensitivity of detecting effects.

73
Q

The phenomenon where participants’ performance improves on a measure over time due to repeated exposure, potentially confounding results in within-groups designs.

A

Practice Effects

74
Q

What types of studies might face issues with carry-over effects?

A

studies where the same participants undergo multiple treatment levels, such as repeated measurements of behaviors or cognitive tasks.

75
Q
A

Advanced research methods UNIsa (7 decks)

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