Questions tagged [main-effects]

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Linear regression: interpreting main effect in the presence of an interaction with a continuous variable

I have a classical multiple regression model: $y=b_0+b_1x_1+b_2x_2+b_3x_1x_2$ where $x_1$ is binary and $x_2$ is continuous and mean-centered. My question is about the interpretation of the estimate $...
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2x2 Mixed Factorial Design Interaction and Main Effects [duplicate]

Hello I am running a 2x2 mixed ANOVA design. My between factor is condition (treatment v control and my within factor is time (pre v post). I am using SPSS and have run my ANOVAs and have found a ...
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Identifying main effects in time series data

I am writing to ask for advice & guidance on how to best extract a main effect within a time series data set. I have time series data (account-month grain) from a SaaS company and have been asked ...
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How to report coefficients for main effects?

Suppose I run an OLS regression as follows: Y = γ1 + γ2*X + γ3*(D*X) where D is a dummy variable. I excluded one dummy variable, so the effect is in γ2 and γ3 must ...
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Anova interaction is non significant but post-hoc is

I run an ANOVA which indicates that room:time is non significant, but post-hoc test shows that time has an effect in room 2 but not in room 1. Is this a consistent result ? What does exactly the ...
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Anova shows a main effect but not the t-test

I run an ANOVA which indicates that a variable is significant, but running a t-test on the same variable shows no significance. Is this a consistent result ? Here are more details of what I did: My ...
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Can a main effect coupled with an interaction, answer my question?

I am currently carrying out a research where I have two categorical predictors with two levels each. The research basically consists of presenting participants with one of four profiles of a target, ...
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How to interpret an interaction in a negative binomial?

I'm investigating the influence of top management team nationality diversity (TMTdiv, continuous between 0-1) and board nationality diversity (Boarddiv, continuous between 0-1) on firm ...
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Why type III sum of squares is not recommended to use when there is significant interaction?

I did notice that in the following example, I obtained a insignificant main effect for treatment when including interaction, although the treatment is clearly effective. Any intuitive explanation? ...
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Factor Levels in a Two-Way ANOVA

I'm trying to figure out the explanations of the Effect Plots below. I believe I have figures out Figure 9a, but the explanation for Figures b, c, and d given in the textbook I do not comprehend. Any ...
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Main effect vs simple effect

In the following linear model: $y = \beta_0 +\beta_1 x_1 +\beta_2 x_2 + \beta_3x_1x_2 + \epsilon$ what are called $x_i$? The term $x_1x_2$ is the interaction. In ANOVA, $x_i$ are named "main ...
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Using Orthogonal Main Effects Plan to select profiles for conjoint analysis

I am trying to create a code in Python to select orthogonal profiles given some attributes and levels. For eg: ...
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Three-way MANOVA Interpretation

I'm interpreting a three-way MANOVA as part of my research, and have come across an interpretation challenge, with three independent variables (Factor A, Factor B, and Factor C) and two dependent ...
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Why are Simple Effects only used when the interaction is significant?

I have conducted a 2-way ANOVA and obtained a significant Main Effect in IV1 and IV2, but a non-significant interaction. Following that, I have analyzed IV1 using a test for Simple Effects and ...
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lmer Dropping levels from summary information in models with main effect and interactions

First off, here are my models: ...
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Interaction effect in logistic regression

I have applied logit model, where my dependent variable is Emp (Employment) 1 if employed otherwise 0, and used level of education and Health status as independent variables where: Education consist 6 ...
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How to handle interaction in this longitudinal model?

Let's assume we have the following result of a longitudinal experiment, which evaluates the effect of a fertilizer over time. The dependent variable is about "poor condition". The higher ...
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Interpreting growth curve analysis (GCA) main effect in light of interaction (eye tracking data)

I have fit parabolas to two groups in a growth curve analysis (GCA) of eye tracking data using orthogonal polynomials (essentially by following Dan Mirman's example for his paper with Magnuson in 2009,...
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