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Table 4 Parameter estimates of EA- and district-random effect models and classical single-level regression model

From: An application of mixed-effect models to analyse contraceptive use in Malawian women

Parameter Mixed effect model Classical linear model
EAREM DREM SLM
Fixed effects
 Intercept −3.962 − 3.817 −3.300
 Parity 0.451 0.417 0.344
 Age − 0.059 −0.056 − 0.045
Place-of-residence(urban)
 Rural −0.211 -0.208 −0.167
Region (northern)
 Central 0.214 0.157 0.189
 Southern 0.164 0.099 0.140
Education (no education)
 Primary 0.381 0.360 0.369
 Secondary 0.635 0.611 0.587
 Secondary 0.704 0.666 0.620
Occupation (not working)
 Manual 0.473 0.453 0.421
 Agriculture 0.368 0.316 0.295
 Business 0.661 0.667 0.564
 Office 0.467 0.433 0.397
Marital-Status (never married)
 Widowed/divorced 1.434 1.479 1.251
 Separation 1.562 1.579 1.342
 Married 2.594 2.597 2.175
Religion (Christian)
 Muslim −0.521 −0.349 −0.544
 Other −0.442 − 0.445 − 0.362
 Religions no religion − 0.264 − 0.215 − 0.171
Random effects
\( {\sigma}_{u(intercept)}^2 \) 0.120 0.086  
\( {\sigma}_{u(age)}^2 \) 0.001 0.0005  
\( {\sigma}_{u(parity)}^2 \) 0.011   
  1. EAREM Enumeration-Area random effect model, DREM District random effect model, SLM Single-level model