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kjetil b halvorsen ♦
kjetil b halvorsen ♦
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This question is based on using a regression for statistical inference (not prediction).

I have conducted hierarchical (logistic mixed effects) regression.

The first model includes the predictors of interest for the study. I am interested in the interactions between Condition and 1) SPQ, 2) CAPS, 3) PDI. There are no significant interactions for any predictor.

performance ~ Condition * (SPQ + PDI + CAPS) + (1 | participant)

The second model includes a list of covariates that are of potential theoretical importance. I wanted to assess whether these affect the (non-significant) interactions from the first model.

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ) + 
                 (1 | participant)

This is obviously a highly complex model, although it does converge. There are significant main effects of Age and IQ and a significant S1*Condition interaction.

How important is parsimony in this case? Should I use backward stepwise elimination comparing log likelihood statistics and report a less complex model?

This question is based on using a regression for statistical inference (not prediction).

I have conducted hierarchical (logistic mixed effects) regression.

The first model includes the predictors of interest for the study. I am interested in the interactions between Condition and 1) SPQ, 2) CAPS, 3) PDI. There are no significant interactions for any predictor.

performance ~ Condition * (SPQ + PDI + CAPS) + (1 | participant)

The second model includes a list of covariates that are of potential theoretical importance. I wanted to assess whether these affect the (non-significant) interactions from the first model.

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ) + (1 | participant)

This is obviously a highly complex model, although it does converge. There are significant main effects of Age and IQ and a significant S1*Condition interaction.

How important is parsimony in this case? Should I use backward stepwise elimination comparing log likelihood statistics and report a less complex model?

This question is based on using a regression for statistical inference (not prediction).

I have conducted hierarchical (logistic mixed effects) regression.

The first model includes the predictors of interest for the study. I am interested in the interactions between Condition and 1) SPQ, 2) CAPS, 3) PDI. There are no significant interactions for any predictor.

performance ~ Condition * (SPQ + PDI + CAPS) + (1 | participant)

The second model includes a list of covariates that are of potential theoretical importance. I wanted to assess whether these affect the (non-significant) interactions from the first model.

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ) + 
                 (1 | participant)

This is obviously a highly complex model, although it does converge. There are significant main effects of Age and IQ and a significant S1*Condition interaction.

How important is parsimony in this case? Should I use backward stepwise elimination comparing log likelihood statistics and report a less complex model?

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Dimitris Rizopoulos
Dimitris Rizopoulos
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This question is based on using a regression for statistical inference (not prediction).

I have conducted hierarchical (logistic mixed effects) regression.

The first model includes the predictors of interest for the study. I am interested in the interactions between Condition and 1) SPQ, 2) CAPS, 3) PDI. There are no significant interactions for any predictor.

performance ~ Condition * (SPQ + PDI + CAPS)+ (1|participant)

performance ~ Condition * (SPQ + PDI + CAPS) + (1 | participant)

The second model includes a list of covariates that are of potential theoretical importance. I wanted to assess whether these affect the (non-significant) interactions from the first model.

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ)+ (1|participant)

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ) + (1 | participant)

This is obviously a highly complex model, although it does converge. There are significant main effects of Age and IQ and a significant S1*Condition interaction.

How important is parsimony in this case? Should I use backward stepwise elimination comparing log likelihood statistics and report a less complex model?

This question is based on using a regression for statistical inference (not prediction).

I have conducted hierarchical (logistic mixed effects) regression.

The first model includes the predictors of interest for the study. I am interested in the interactions between Condition and 1) SPQ, 2) CAPS, 3) PDI. There are no significant interactions for any predictor.

performance ~ Condition * (SPQ + PDI + CAPS)+ (1|participant)

The second model includes a list of covariates that are of potential theoretical importance. I wanted to assess whether these affect the (non-significant) interactions from the first model.

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ)+ (1|participant)

This is obviously a highly complex model, although it does converge. There are significant main effects of Age and IQ and a significant S1*Condition interaction.

How important is parsimony in this case? Should I use backward stepwise elimination comparing log likelihood statistics and report a less complex model?

This question is based on using a regression for statistical inference (not prediction).

I have conducted hierarchical (logistic mixed effects) regression.

The first model includes the predictors of interest for the study. I am interested in the interactions between Condition and 1) SPQ, 2) CAPS, 3) PDI. There are no significant interactions for any predictor.

performance ~ Condition * (SPQ + PDI + CAPS) + (1 | participant)

The second model includes a list of covariates that are of potential theoretical importance. I wanted to assess whether these affect the (non-significant) interactions from the first model.

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ) + (1 | participant)

This is obviously a highly complex model, although it does converge. There are significant main effects of Age and IQ and a significant S1*Condition interaction.

How important is parsimony in this case? Should I use backward stepwise elimination comparing log likelihood statistics and report a less complex model?

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SilvaC
SilvaC
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Is parsimony crucial for statistical inference?

This question is based on using a regression for statistical inference (not prediction).

I have conducted hierarchical (logistic mixed effects) regression.

The first model includes the predictors of interest for the study. I am interested in the interactions between Condition and 1) SPQ, 2) CAPS, 3) PDI. There are no significant interactions for any predictor.

performance ~ Condition * (SPQ + PDI + CAPS)+ (1|participant)

The second model includes a list of covariates that are of potential theoretical importance. I wanted to assess whether these affect the (non-significant) interactions from the first model.

performance ~ Condition * (SPQ + PDI + CAPS+ S1 + S2 + Age + IQ)+ (1|participant)

This is obviously a highly complex model, although it does converge. There are significant main effects of Age and IQ and a significant S1*Condition interaction.

How important is parsimony in this case? Should I use backward stepwise elimination comparing log likelihood statistics and report a less complex model?