ADVANCEMENT OF THE SCIENCE
home?” Response options were the following: never, once or twice, every week, or every day. Finally, participants reported how often they were in places that smelled of stale tobacco smoke by responding to the question, “In the past month, about how often have you spent time in places that smelled of stale tobacco smoke?” Response options were the following: never, once or twice, every week, or every day. Knowledge To assess knowledge of the risks of THS expo- sure, participants were asked to respond to eight statements, such as: “Thirdhand smoke contains dangerous chemicals,” “Thirdhand smoke can be found in the dust of homes where people have smoked,” and “Thirdhand smoke can make kids sick.” Response options were on a 5-point Likert-type scale from strongly disagree to strongly agree (Record et al., 2022). Scale score distributions were approximately normal, and Cronbach’s alpha supported high internal consistency ( α = .865, m = 4.37, SD = 0.64). Attitudes To assess attitudes toward THS risks, partici- pants were asked to respond to seven state- ments, including: “Hospitals should hire only nonsmokers,” “Sellers should be required to disclose if someone has smoked in their home,” and “Childcare providers should be nonsmokers.” Response options were on a 5-point Likert-type scale from strongly dis- agree to strongly agree (Record et al., 2022). Score distributions were approximately nor- mal with supported internal consistency of the measure ( α = .839, m = 4.04, SD = 0.80). E cacy Following Bandura (1977), ecacy was con- ceptualized as the perceived ability to suc- cessfully avoid THS exposure. To assess e- cacy, four items were adapted from Sherer et al. (1982). All items responded to the stem, “I am able to . ” Example items included: “avoid exposure to thirdhand smoke,” “determine if a place is smoke free,” and “ask people not to smoke around me.” Response options were on a 5-point Likert-type scale from strongly disagree to strongly agree. Score distributions were approximately nor- mal, and Cronbach’s alpha supported low but acceptable measure internal consistency ( α = .0719, m = 3.57, SD = 0.90).
TABLE 1
Demographics, Smoking Behaviors, and Characteristics Known to Be Related to Thirdhand Smoke Exposure Among Parents in California ( N = 363)
Age Groups of Children in the Household*
Overall # (%)
All Children <13 Years ( n = 120)
All Children 13–17 Years ( n = 136)
All Children <18 Years ( n = 107)
Gender
Female
100
116
89
305 (84)
Male
20
16
9 9
45 (12)
Other
0
4
13 (4)
Hispanic or Latinx Yes
36 84
41 89
33 69
110 (30) 242 (67)
No
Choose not to answer
0
6
5
11 (3)
Race (select all that apply) African American/Black
10 12
14 24
5
29 (8)
Asian American
15
51 (14)
Hawaiian/Pacific Islander
2 3
1 8
4
7 (2)
Native American/Alaska Native
12 68 14
23 (6)
White Other
86 11
85 15
239 (66)
40 (11)
Age in M (SD ) Smoking status Smoker
39 (14.66)
35 (18.13)
37 (16.09)
37 (16.09)
11
8
7
26 (7)
Nonsmoker
109
128
100
337 (93)
Education Some high school or high school diploma/GED Some college or associate degree
22
40
36
98 (27)
56 26 16
69 17 10
53 14
178 (49)
Bachelor’s degree Graduate degree
57 (16)
4
30 (8)
continued
and 56% explained variance (eigenvalue = 2.80). Finally, scale score distributions were approximately normal, and Cronbach’s alpha supported internal consistency ( α = .802, m = 3.79, SD = 0.94). Demographic and Exposure Characteristics Standard demographic characteristics (e.g., gender, age, race, ethnicity, educational attainment, employment status) were col- lected from participants. In addition, char- acteristics known to be related to possible
Behavior To assess protective behaviors to prevent THS, participants were asked to respond to five statements, such as: “I would buy fur- niture from a smoker,” “I would buy a car that has been smoked in,” and “In general, I avoid places where people have smoked.” Response options were on a 5-point Likert- type scale from strongly disagree to strongly agree (Record et al., 2022). Principal compo- nent factor analyses revealed high first-factor saturation supporting single-factor loadings
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Volume 86 • Number 7
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