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Construal Level International Multilab Replication (CLIMR) Project: Influence of Actual Distance on the Spatial Distance Effect

CLIMR Team 2024-12-05

Does the actual distance between the cities used in the spatial distance manipulation influence the effect of the manipulation on the BIF?

lrt_km_spatial
## Data: data_bif_spatial %>% filter(complete.cases(haversine))
## Models:
## glmm_spatial_km_base: bif ~ condition + (1 | lab:sub) + (1 | lab) + (1 | item)
## glmm_spatial_km_add: bif ~ condition + haversine_rescale + (1 | lab:sub) + (1 | lab) + (1 | item)
## glmm_spatial_km_int: bif ~ condition * haversine_rescale + (1 | lab:sub) + (1 | lab) + (1 | item)
##                      npar   AIC   BIC logLik deviance  Chisq Df Pr(>Chisq)
## glmm_spatial_km_base    5 44984 45027 -22487    44974                     
## glmm_spatial_km_add     6 44984 45036 -22486    44972 1.7054  1     0.1916
## glmm_spatial_km_int     7 44986 45046 -22486    44972 0.3783  1     0.5385

summary(glmm_spatial_km_base)
## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
##  Family: binomial  ( logit )
## Formula: bif ~ condition + (1 | lab:sub) + (1 | lab) + (1 | item)
##    Data: data_bif_spatial %>% filter(complete.cases(haversine))
## 
##      AIC      BIC   logLik deviance df.resid 
##  44983.9  45026.7 -22486.9  44973.9    38437 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9148 -0.8235  0.4210  0.6499  2.9666 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  lab:sub (Intercept) 0.77348  0.8795  
##  lab     (Intercept) 0.06563  0.2562  
##  item    (Intercept) 0.46114  0.6791  
## Number of obs: 38442, groups:  lab:sub, 2958; lab, 77; item, 13
## 
## Fixed effects:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       0.70143    0.19284   3.637 0.000275 ***
## conditiondistant  0.04446    0.04030   1.103 0.270005    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## condtndstnt -0.104

summary(glmm_spatial_km_add)
## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
##  Family: binomial  ( logit )
## Formula: bif ~ condition + haversine_rescale + (1 | lab:sub) + (1 | lab) +      (1 | item)
##    Data: data_bif_spatial %>% filter(complete.cases(haversine))
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  44984.2  45035.5 -22486.1  44972.2    38436 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9128 -0.8230  0.4211  0.6498  2.9622 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  lab:sub (Intercept) 0.77374  0.8796  
##  lab     (Intercept) 0.06287  0.2507  
##  item    (Intercept) 0.46109  0.6790  
## Number of obs: 38442, groups:  lab:sub, 2958; lab, 77; item, 13
## 
## Fixed effects:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        0.702767   0.192794   3.645 0.000267 ***
## conditiondistant   0.044552   0.040310   1.105 0.269063    
## haversine_rescale -0.003359   0.002548  -1.318 0.187409    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndtnd
## condtndstnt -0.104       
## havrsn_rscl -0.005 -0.002

summary(glmm_spatial_km_int)
## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
##  Family: binomial  ( logit )
## Formula: bif ~ condition * haversine_rescale + (1 | lab:sub) + (1 | lab) +      (1 | item)
##    Data: data_bif_spatial %>% filter(complete.cases(haversine))
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  44985.8  45045.7 -22485.9  44971.8    38435 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9110 -0.8230  0.4208  0.6496  2.9683 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  lab:sub (Intercept) 0.77355  0.8795  
##  lab     (Intercept) 0.06288  0.2508  
##  item    (Intercept) 0.46111  0.6790  
## Number of obs: 38442, groups:  lab:sub, 2958; lab, 77; item, 13
## 
## Fixed effects:
##                                     Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                         0.702809   0.192681   3.648 0.000265 ***
## conditiondistant                    0.044361   0.040305   1.101 0.271055    
## haversine_rescale                  -0.004233   0.002915  -1.452 0.146503    
## conditiondistant:haversine_rescale  0.001745   0.002827   0.617 0.537111    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndtnd hvrsn_
## condtndstnt -0.104              
## havrsn_rscl -0.004  0.002       
## cndtndstn:_  0.000 -0.007 -0.486

Does the actual distance between the cities used in the spatial distance manipulation influence the strength of the spatial distance manipulation?

meta_spatial_mc_km
## 
## Multivariate Meta-Analysis Model (k = 77; method: REML)
## 
## Variance Components: none
## 
## Test for Residual Heterogeneity:
## QE(df = 75) = 157.1653, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 17.6278, p-val < .0001
## 
## Model Results:
## 
##            estimate      se     zval    pval   ci.lb   ci.ub      
## intrcpt      1.0007  0.0590  16.9737  <.0001  0.8852  1.1163  *** 
## haversine    0.0001  0.0000   4.1985  <.0001  0.0001  0.0002  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

knitr::include_graphics("figures/climr_spatial-actual-distance-cause-size.png")