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Table 3 Family demographic characteristics by eating behavior profile

From: Characteristics of eating behavior profiles among preschoolers with low-income backgrounds: a person-centered analysis

 

Total Sample

High Food Avoidant

High Food Approach

Moderate

Eating

P

  

Profile 1

Profile 2

Profile 3

 
 

N = 1004

n = 357 (35.6%)

n = 213 (21.2%)

n = 434 (43.2%)

 

Child age, months, M (SD)

49.7 (6.3)

5.0 (6.3)

49.4 (6.7)

49.7 (6.1)

.50

Child sex, n (%)

    

.14

Male

494 (49.3)

174 (48.9)

94 (44.1)

226 (52.3)

Female

507 (50.7)

182 (51.1)

119 (55.9)

206 (47.7)

Child race and ethnicity, n (%)

    

.13

White, Non-Hispanic

511 (51.1)

183 (51.6)

113 (53.1)

215 (49.8)

Black, Non-Hispanic

246 (24.6)

77 (21.7)

61 (28.6)

108 (25.0)

Hispanic and/or other race*

243 (24.3)

95 (26.8)

39 (18.3)

109 (25.2)

Maternal race and ethnicity, n (%)

    

.25

White, non-Hispanic

617 (61.6)

228 (64.0)

122 (57.6)

267 (61.5)

Black, non-Hispanic

246 (24.6)

74 (2.8)

60 (28.3)

112 (25.8)

Hispanic and/or other race*

139 (13.9)

54 (15.2)

30 (14.2)

55 (12.7)

Maternal education, n (%)

    

.69

 ≤ HS Grad/GED

477 (47.8)

173 (48.9)

112 (52.6)

192 (44.4)

 > HS Grad/GED

522 (52.3)

181 (51.1)

101 (47.4)

240 (55.6)

Marital status, n (%)

    

.48

Single parent

392 (43.6)

126 (40.7)

85 (44.3)

181 (45.5)

Married

260 (28.9)

88 (28.4)

54 (28.1)

118 (29.7)

Committed relationship

248 (27.6)

96 (31.0)

53 (27.6)

99 (24.9)

Household income-to-needs ratio, M (SD)

.86 (.64)

.89 (.71)

.79 (.56)

.87 (.62)

.24

Household Food Insecurity, n (%)

    

.04

Food Secure

665 (67.3)

240 (67.6)a, b

126 (60.3)b

299 (70.5)a

Food Insecure

323 (32.7)

115 (32.4)

83 (39.7)

125 (29.5)

  1. Differing superscript letters indicate differences between eating behavior profiles (P < .05); superscript letters that are the same indicate no difference between eating behavior profiles post-hoc analyses
  2. *Hispanic and non-Hispanic multiracial or other race were combined for analysis given the small sample sizes
  3. Predicting profile membership using multinomial regression and “food insecure” as the referent