Research ArticlePSYCHOLOGY

# Moving chairs in Starbucks: Observational studies find rice-wheat cultural differences in daily life in China

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Vol. 4, no. 4, eaap8469

### Tables

• Table 1 Rice-wheat differences in sitting alone.

Rice-wheat differences in sitting alone.. Note that day of the week is coded numerically: Monday, 1 to Sunday, 7. Time of day is rounded to the nearest hour. Model is a hierarchical linear model (HLM) using the binomial GLMER function. Data are grouped at the city level in each model except the model with district GDP per capita. Table S10 presents models with districts nested within cities.

 B/γ SE z P Percent rice −0.42 0.16 −2.57 0.010 Time of day −0.07 0.01 −6.55 <0.001 Day of the week −0.07 0.01 −6.46 <0.001 Percent rice −0.43 0.14 −2.97 0.003 Time of day −0.07 0.01 −6.77 <0.001 Day of the week −0.07 0.01 −6.38 <0.001 International chain 0.17 0.06 3.05 0.002 Percent rice −0.45 0.17 −2.70 0.007 Time of day −0.06 0.01 −6.60 <0.001 Day of the week −0.07 0.01 −6.62 <0.001 City GDP per capita 0.018 0.005 3.38 0.001 Percent rice −0.56 0.09 −6.37 <0.001 Time of day −0.07 0.01 −5.50 <0.001 Day of the week −0.07 0.01 −5.82 <0.001 City population density 0.02 0.03 0.59 0.559 Percent rice −0.51 0.22 −2.33 0.020 Time of day −0.07 0.01 −5.65 <0.001 Day of the week −0.07 0.01 −5.87 <0.001 District GDP per capita 0.010 0.006 1.74 0.082 Percent rice −0.53 0.16 −3.39 0.001
• Table 2 Rice, GDP, and demographic predictors of chair moving.

Note that models are HLMs using the binomial GLMER function. Data are grouped at the city level, except for the bottom two models, which are grouped at the district level. See table S11 for models with districts nested in cities.

 B/γ SE z P Percent rice −1.86 0.44 −4.19 <0.001 Employee 1.93 0.30 6.52 <0.001 Below 40 years old 0.03 0.38 0.09 0.928 Female (civilians only) −1.06 0.44 −2.41 0.016 Employee 2.03 0.31 6.57 <0.001 Female −0.46 0.30 −1.51 0.131 Percent rice −2.02 0.48 −4.24 <0.001 Employee 2.03 0.35 5.80 <0.001 Female −4.53 0.31 −1.47 0.141 City GDP per capita (10,000 RMB) −0.02 0.17 −0.13 0.895 Percent rice −1.99 0.51 −3.91 <0.001 Employee 2.07 0.32 6.53 <0.001 Female −0.46 0.32 −1.46 0.145 District GDP per capita (10,000 RMB) 0.02 0.02 0.94 0.347 Percent rice −2.30 0.62 −3.69 <0.001 Employee 2.10 0.32 6.65 <0.001 Female −0.41 0.32 −1.27 0.205 District population density −0.01 0.10 −0.12 0.902 Percent rice −1.96 0.74 −2.64 0.008

### Supplementary Materials

fig. S1. Sample chair trap in a Starbucks in Shanghai.

table S1. Are people in international chains more likely to be sitting alone?

table S2. Rice-wheat differences controlling for international chain.

table S3. Sitting alone and GDP.

table S4. Sitting alone and district-level data.

table S5. Basic predictors of chair moving.

table S6. City and district census predictors of chair moving.

table S7. International comparison of chair moving.

table S8. How well do other major theories of culture predict sitting alone?

table S9. How well do other major theories of culture predict chair moving?

table S10. Sitting alone models with stores nested in districts nested in cities.

table S11. Chair moving models with stores nested in districts nested in cities.

table S12. Chair moving models with stores nested in districts nested in cities.

section S1. Rice statistics

section S2. Chair moving

section S3. Controlling for observer

section S4. Hong Kong GDP per capita

section S5. Age in districts

section S6. Calculating effect sizes in GLMER

section S7. Graphing mean percent sitting alone

section S8. GDP per capita

section S9. Alternative predictors

section S10. Chair moving validity checks

section S11. Ethics statement

References (41)

• ## Supplementary Materials

This PDF file includes:

• fig. S1. Sample chair trap in a Starbucks in Shanghai.
• table S1. Are people in international chains more likely to be sitting alone?
• table S2. Rice-wheat differences controlling for international chain.
• table S3. Sitting alone and GDP.
• table S4. Sitting alone and district-level data.
• table S5. Basic predictors of chair moving.
• table S6. City and district census predictors of chair moving.
• table S7. International comparison of chair moving.
• table S8. How well do other major theories of culture predict sitting alone?
• table S9. How well do other major theories of culture predict chair moving?
• table S10. Sitting alone models with stores nested in districts nested in cities.
• table S11. Chair moving models with stores nested in districts nested in cities.
• table S12. Chair moving models with stores nested in districts nested in cities.
• section S1. Rice statistics
• section S2. Chair moving
• section S3. Controlling for observer
• section S4. Hong Kong GDP per capita
• section S5. Age in districts
• section S6. Calculating effect sizes in GLMER
• section S7. Graphing mean percent sitting alone
• section S8. GDP per capita
• section S9. Alternative predictors
• section S10. Chair moving validity checks
• section S11. Ethics statement
• Reference (41)