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In 2010, an estimated 4.7% of people aged 60+ had dementia (Sosa-Ortiz, Acosta-Castillo, & Prince, 2012), leading to a global economic burden estimated at $604 billion (Wimo, Jonsson, Bond, Prince, & Winblad, 2013). The greatest predictor of dementia is increasing age (Plassman et al., 2007); as the number of older adults in the population continues to grow (Sosa-Ortiz, Acosta-Castillo, & Prince, 2012), early identification of neurodegenerative disease is increasingly essential. The rise in dementia prevalence creates the need for parallel improvements in treatment and palliative care. Research investment in the early characterization and treatment of dementia is an important public health goal. One challenge related to the early detection of dementia continues to be the identification of subtle cognitive changes in what is considered its “pre-clinical” phase. These subtle cognitive deficits may occur years before a clinical diagnosis is made, as biomarker evidence suggests that Alzheimer’s disease (AD) and related pathologies accumulate decades before clinical diagnosis (Albert et al., 2011; Howieson et al., 2008; Sperling et al., 2011). Detection of the earliest and most subtle cognitive changes should coincide with detection of the earliest and most subtle functional changes, which affect an individual’s ability to perform daily tasks in the natural environment (Chaytor & Schmitter-Edgecombe, 2003). A promising area for additional research in this field involves the latent dementia construct referred to as “δ” by Royall and colleagues (Royall & Palmer, 2012; Royall, Palmer, & O’Bryant, 2012), which may represent a recent advancement toward the goal of detecting early and subtle cognitive changes and concomitant functional decline. This construct, which is derived from Spearman’s general intelligence factor g (Spearman, 1904), represents a further subdivision of Spearman’s g into the two independent factors g’ (g prime) and δ. Royall et al. describe the δ construct, the focus of the current investigation, as representing the “cognitive correlates of functional status” (Royall et al., 2007; Royall & Palmer, 2012); as such, g’ reflects the component of one’s general cognitive ability that is separate from the functional decline caused by neurodegenerative disease. The δ and g’ constructs differ only slightly. Though both are thought to represent the latent variables underlying cognitive task performance, only δ is theorized to underlie one’s ability to function independently and perform activities of daily living (ADL). According to Royall and colleagues, g’ explains the largest proportion of the variance in cognitive test results, but δ most strongly correlates with dementia severity (Royall & Palmer, 2012). Most approaches to cognitive assessment of dementia focus on observed scores, which contain measurement error. In contrast, the latent variable modeling approach taken by Royall, which utilizes confirmatory factor analysis (CFA), can be used to quantify dementia severity with error in estimation but not in measurement (McArdle, 2009; Weston & Gore, 2006;). Latent variable models that fit the data well can be interpreted as providing evidence for a latent trait underlying observed test scores. The model proposed by Royall and colleagues should not be dependent upon the use of specific cognitive tests to measure the construct of dementia, which has a categorical latent structure (Gavett & Stern, 2012). If the model proposed by Royall and colleagues provides a valid representation of the latent dementia phenotype, then δ represents an important construct that could advance neurodegenerative disease research, especially as it pertains to the use of cognitive tests to aid in the detection of the earliest co-occurring changes in cognition and ADLs (Albert et al., 2011; Howieson et al., 2008; Sperling et al., 2011). To ensure that the δ construct is invariant to sample and assessment methods (i.e., cognitive test battery), Royall and colleagues’ model for δ requires validation in other samples and with different cognitive tests. The hypothesized model, which treats δ and g’ as independent constructs, has been validated in demented individuals (Royall, Palmer, & O’Bryant, 2012), cognitively healthy adults (Royall & Palmer, 2012) and in an ethnically diverse sample (Royall & Palmer, 2013). The goal of the current study is to further cross-validate the δ construct in a national clinical case series of older adult participants from the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) (Beekly, Ramos, & Lee, 2007; Morris et al., 2006; Weintraub et al., 2009), both cross-sectionally and longitudinally. The UDS, maintained by NACC, contains longitudinal assessment data that reflect both the cognitive and functional changes that occur in healthy aging and as a result of AD, mild cognitive impairment (MCI), and other neurodegenerative diseases (Morris et al., 2006). As such, the UDS provides a unique opportunity for cross-validation of the δ construct through use of its large, diverse, and cognitively heterogeneous sample. In addition, the UDS includes a different battery of neuropsychological and functional tests than Royall’s initial δ validations (Royall & Palmer, 2012; Royall & Palmer, 2013; Royall, Palmer, & O’Bryant, 2012) and maintains approximately annual data relevant to ADLs and multiple cognitive domains, including attention, speed of processing, executive function, episodic memory, language, and behavioral symptoms (Weintraub et al., 2009). In validating the δ construct cross-sectionally, we hypothesize that the Royall et al. (Royall & Palmer, 2012; Royall, Palmer & O’Bryant, 2012) model, where δ represents the latent variable underling both cognitive and functional status and g’ represents the latent variable underlying the component of cognitive ability not related to functional decline, will fit the baseline data from the NACC UDS well. In cross-validating the δ construct longitudinally, we hypothesize that participants’ latent dementia status, as measured by δ on an approximately annual basis, will change in conjunction with participants’ latent dementia status as measured approximately annually by the Clinical Dementia Rating Sum of Boxes (CDR-SB), one of the most commonly used methods of rating an individual’s cognitive and functional status in the context of a neurodegenerative disease (O’Bryant et al., 2010).