consisted of assessing the factorial validity (confirmatory factor analysis or CFA) for all constructs used in all measurement models. For this, I will also need to conduct an exploratory data analysis to determine how each of the following affects statistics: missing data (and the need to deal with missing data through multiple imputation techniques), outliers, nonlinearity, and non-normality. Subsequently, the adequacy of indicators for the latent variables vas verified through CFA for each of the construct in the conceptual model. In this step, careful attention was paid to the statistical significance of factor loadings (p<0.05 level) and the size of the loading (<.10), to make sure all indicators loaded significantly onto their respective factor or construct. SEM model fit indices were used to determine goodness of fit of the different models and then compare these indexes to determine the most theoretically useful model (Tabachnick & Fidell, 2008). In particular, the following absolute model fit indices were used to evaluate model fit: the Chi-square-to-degrees of freedom (<6 as acceptance value) and root mean square error of approximation (RMSEA, with .08 as conservative cut-off value).
The first CFA model evaluated was social position with the following indicators: education level (high school = 1, less than high school =0), marital status (married=1, unmarried=0), race/ethnicity status (non-Hispanic white=1, race/ethnicity minority=0), language status (English=1, other than English=0), nativity (US born=1, foreign born=0), perceived social standing (social ladder perception, range 0-10), and percentage of federal poverty line (calculated from self-reported annual household income and number of dependents). Of these, only education, race/ethnicity status, nativity, and language loaded significantly onto the factor “social position”, which were selected as indicators to model social position.
The CFA for early life adversity was measured by summated Adverse Childhood Experiences items (ACE score), with consideration of their underlying theory of the instrument. In particular, the ACE score is a checklist of yes/no questions that measure self-reported child abuse, neglect, and household dysfunction that occurred before 18 years of life. It is important to notice that ACE items are the result of
categorizing an underlying continuous variable though. For example, the first ACE item reads: “Did a parent or other adult in the household often or very often… Swear at you, insult you, put you down, or humiliate you? Or act in a way that made you afraid that you might be physically hurt?” Furthermore, this instrument has not been used in CFA. For such reasons, parceling was used to ameliorate the effects of categorized and non-normally distributed item-level data (Little, Cunningham, Shahar, & Widaman, 2002). This procedure of using summed scale items is a technique often used in structural equation models (Sass & Smith, 2006). Parceling is defined as the sum (or average) of two or more items of a scale, with the purpose of creating an aggregate-level indicator (Little et al., 2002), which provides psychometric and modeling-related benefits when the scale is unidimensional (Matsunaga, 2008). Thus, parceling was conducted as follows: ACE items (ace1-3) that measured emotional, physical, and sexual abuse were summed; ACE items (ace4-5) on child neglect; ACE items (ace6-10) on household
dysfunction. The resulting CFA model of ACE demonstrated perfect fit (RMSEA=.000) because the model was just identified, but all factor loadings were statistically significant.
The CFA for maternal health was constructed based on the Euroqol instrument (EQ-5D): self- rated health (Euroqol visual analog scale 0-100); health-related quality of life score generated with default Euroqol preference-based valuation algorithm (R. Rabin & de Charro, 2001). To assure the rule of thumb of at least three indicators per latent variable in SEM models (Diamantopoulos, 2000), maternal health was indicated by self-rated health, health-related quality of life score, plus two binary items of the EQ-5D (self-care and mobility). This model achieved adequate model fit and all factor loadings were significant. The CFA model for perceived stress using the 4 items from the PSS scale, plus a binary measure of pregnancy intention (unwanted=1, wanted=0) presented a bad fit initially (RMSEA >0.08). Because the “pss2” did not load significantly, this indicator was deleted. As result the model exhibited perfect fit (just identified model, RMSEA =.000), and after such modification the model was considered satisfactory for the next step. In a similar fashion, the CFA for experiences of discrimination as measured by the summated scores of items eod1-3, eod4-6, and items eod7-9, demonstrated perfect fit (RMSEA=.000) and all factor loadings were significant.
The CFA model for social support as measured by the 5 items from the Brief MOS Social Support Measure, plus the numbers of friends/relatives showed a bad fit initially (RMSEA>.08).
However, all factor loadings were statistically significant. Acceptable model fit was achieved by allowing a correlation between items “ssup1” (i.e. someone to confide in or talk to about yourself or your
problems) with “ssup2” (someone to share your most private worries and fears with), which was theoretically plausible since these items both measured emotional support.
The CFA for intimate partner violence, measured by the 4 items from the HITS instrument, consistently exhibited bad fit (RMSEA>0.09), even after fixing and freeing parameters (model
modification techniques resulted in non-convergence). Thus, this model was excluded from the structural analyses.
The CFA for father involvement was measured by six newly designed father involvement questions (Likert type items). In addition, two categorical questions were added: adequacy of father involvement (question: “What do you want the baby’s father involvement to be compared to now?”), and father presence (question: “Are you currently living with the baby’s father?”). This model had an excellent fit, as indicated by RMSEA of 0.04, CFI=.98, TLI=.98, SRMS=.02. All factor loadings were statistically significant, which indicated that this model was acceptable for the next step.
The CFA for maternal maladaptive behaviors was measured with the frequency of smoking and alcohol use 3 months before pregnancy and during pregnancy. In addition, as a third indicator, a
categorical variable obesity was used (binary variable from BMI>30 =1, or BMI<30=0). This model was just identified, and thus, exhibited perfect fit.
The CFA for perinatal health was measured by gestational age, birth weight (in pounds), and the Healthy Start Infant Risk Screen score. Gestational age and birth weight are typical indicators of perinatal health and morbidity. However, a latent variable model (CFA) with two indicators yields a non-identified model. The issue of non-identification occurs when more equations (parameters) are being estimated than the covariances available (data points in the covariance matrix). To prevent this issue, a rule of thumb indicates that at least three indicators per latent variable should be modeled. For that reason, the healthy
start risk screen score was used for this purpose. The healthy start infant risk scoring system (variable ‘hirs’) is a measure of infant morbidity and mortality risk used to recommend services to the infant post- neonatally (Florida Department of Health, 2007). Hence, it was considered that a latent variable referred as “perinatal health” could be an underlying variable for the infant risk screen, and that “healthy start infant risk screen” could be a reflective indicator (Coltman, Devinney, Midgley, & Venaik, 2008). Other measures that could be used instead were not available, since birth outcome data was limited to Healthy Start Administrative databases. This CFA model showed perfect fit due to a just-identified model. All factor loadings were statistically significant and demonstrated large effect sizes in the expected direction, as follows: gestational age (factor loading = 0.95), birth weight (factor loading = 0.75), and healthy start infant risk score (factor loading = -0.596). Therefore, this model was also suitable for the next step, structural equation models.