The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Identification of Risk and Protective 

Factors for Elder Financial Exploitation 

Author(s): Stacey Wood, Ph.D. 

Document Number: 251034 

Date Received: July 2017 

Award Number: 2013-IJ-CX-0026

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
1

Overview: Final Summary 

Title: Identification of Risk and Protective Factors for Elder Financial Exploitation

Principal Investigator: Stacey Wood, PhD 

Purpose: Financial exploitation of older adults (FE) is a highly significant social problem 

that, to date, has not received much attention from the field of psychology. Data sources 

tracking FE report that FE has been increasing with losses of approximately $2.9 billion 

dollars per year. Psychological risk factors are well established and psychological 

outcomes have recently been demonstrated. FE can occur at any stage of the lifespan and 

the literature regarding prevalence amongst older adults has been mixed in terms of 

supporting a theory that older adults are more “susceptible” to fraud. However, there has 

been literature with documentation that older adults are targeted disproportionately, and 

are less likely to report FE.

 The purpose of this project was to develop a conceptual model that includes risk 

and protective factors for FE. In order to accomplish this goal, we collected data on 

individual difference variables with an emphasis on cognitive factors and data on 

contextual factors using an individually administered survey approach. The framework 

for this project was derived from known risk factors for FE, predicted protective factors 

for FE, and conceptual approaches from the child mistreatment literature on risk and 

resilience. The project took place over two waves of data collection, each of 

approximately 200 older adults.

The initial goals of this project were to collect a large enough sample to: (1) 

Examine control factors, risk factors, and protective factors for FE in a correlational 

approach as a first step, (2) To examine subtypes of risk and elder abuse subtypes based 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
2

on the Conrad Older Adult Financial Exploitation Measure (OAFEM), and (3) Examine 

more complex models that included interactions of risk and protective variables. 

Project Participants: 

Three hundred and ninety-five community-dwelling adults aged 60 years and above were 

recruited from the Greater Los Angeles (LA) Area via an existing participant pool, or 

with flyers distributed to senior centers and retirement communities in two waves. 

Participants’ average age was 73.38 (SD = 7.91) with 15.26 years of education (SD = 

3.18). Table 1 includes further demographic information.

Project Design: 

Goal 1: Identifying Risk Factors and Protective Factors for FE

The first step of this project was to identify risk factors and potential protective 

factors in a community sample of 200 individuals. Variables of interest included

cognitive variables (MMSE, executive functioning, memory), financial management 

skills (numeracy, calculation), protective factors (number of people in social networks, 

social participation, perceived social isolation, interpersonal support, positive and 

negative interaction with close others) and control variables (demographics, living 

arrangement, IADLS, depression, self-report health). The dependent measure was their 

score on Conrad et al.’s (2010) 79-item OAFEM. This measure served as the dependent 

measure for susceptibility to FE. 

Bivariate linear correlation was used to examine the relationship between 

interested predictors and FE. Relatively younger adults were more likely to report FE in 

our sample (r = -.12, p = .02), so did males (r = .11, p = .02). Risk factors known from 

the literature were all significantly correlated with FE including depression (r = .22, p

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
3

< .001), worse physical health (r = -.17, p = .001), and IADL needs (r = -.16, p = .001) 

were associated with FE. However, while overall cognition (MMSE) was not correlated 

with FE, both immediate recall (r = -.12, p = .02) and delayed recall were significantly 

related (r = -.11, p = .03). Numeracy, or literacy for numbers, was a unique predictor 

after known risk factors were accounted for and is described further under Goal 3. As for 

the protective social factors, perceived social support (r = -.12, p = .02), isolation (r = .20, 

p < .001), and negative interactions with close others (r = .12, p = .02) were correlated 

with FE, indicating the potential to target older adults’ social network in building 

intervention and prevention programs. 

GOAL 2: Examine subtypes of FE types 

For goal two we included the complete sample of 395 participants after data 

cleaning. The OAFEM contains 79 items of FE statements that capture six categories of 

FE (abuse of trust, financial entitlement, coercion, theft and scams, signs of possible 

abuse, and risk factors; Conrad, Iris, Ridings, Langley, & Wilber, 2010). Participants 

reported whether each statement occurred to them in the past 12 months. Positive 

endorsement received a score of two, suspicion of exploitation received a score of one, 

and negative endorsement received a score of zero. Responses could range from 0, 

indicating no exploitation, to 158 if all 79 items were endorsed. In our first paper, the 

mean OAFEM score was 7.26, which was too low a score to allow for more nuanced 

analysis of fraud subtypes. 

In the complete sample of 395 individuals, the mean OAFEM score was 7.66. 

This low number of endorsements precluded further analysis by subtype. Descriptive 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
4

statistics revealed the most common endorsement was “entitlement” (M = 3.34) followed 

by “signs of possible abuse” (M = 2.63).

Future research on risk for different types of FE may need to include substantiated 

victims, such as Adult Protective Services’ clients. In our mild, community based sample, 

we did not end up having enough power to explore these questions beyond the descriptive 

information above indicating entitlement was the most common subtype. 

GOAL 3: Model Building and Interaction of Risk and Protective Variables

For the third goal of the project, we proposed building models that incorporated 

all of our variables to look at possible interactions of our variables. With a sample size of 

395, power was too low when we include all proposed variables. Therefore, we decided 

to work on two separate papers—one focusing on all the neurocognitive variables as 

predictors, and the other focusing on all the social variables as predictors. 

In our first paper published in the Journal of Gerontology: Psychological 

Sciences titled, “Importance of numeracy as risk factors for financial elder exploitation in 

a community sample,” we used a OLS regression analysis approach to examine primarily 

the cognitive variables related to FE risk (Wood, Liu, Hanoch, & Estevez-Cores, 2015). 

This initial paper included the first wave of 200 participants. On average, participants’ 

OAFEM score was 7.26, while the median was 4.00. Our sample contained more females 

(70.1%), Caucasians (83.6%), and participants who were married or lived with a partner 

(60.2%). Empirically, no score has been identified as a cut-off point for elder FE

substantiation on the OAFEM. With the possible range of 0 to 158, the mean of 7.26 

indicated few elder FE cases were at severe levels. One can conceptualize the OAFEM 

on a continuum with risk for fraud and mild symptoms at the low end and substantiated 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
5

severe cases at the high end. Our sample of community dwelling individuals with a mean 

score of 7.26 clearly represent the mild end with many individuals (64 out of 200

participants endorsing “no” for all 79 items). 

High numeracy was found to be a significant predictor of decreased risk after 

controlling for other demographic variables. Less numerate participants reported risk of 

experiencing FE significantly more frequently. Importantly, numeracy remained a 

significant predictor in the presence of other risk factors, including dependency, physical 

and mental health, as well as overall cognition. Our findings, thus, are aligned with other 

researchers’ (e.g., Hung, Parker, & Yoong, 2009; Reyna, Nelson, Han, & Dieckmann, 

2009) argument that numeracy acts as an independent factor in the medical and financial 

domain. Interestingly, executive functioning was not related to FE in this sample. 

Executive functioning has been reported as a critical domain in other studies of FE and 

we expected that to be the case here as well (Wood et al., 2014). Executive functioning 

can include a wide range of assessment tools that may tap reasoning, impulse control, 

flexibility, and planning. In this relatively high functioning sample, it may be that the 

tasks we chose were not sensitive enough to provide discrimination and/or did not tap 

into the right executive process as executive functioning can encompass many control 

processes. 

In summary, we examined cognitive risk factors in the presence of demographic 

and other well-known predictors in the first paper. In this sample, we replicated previous 

findings regarding risk (self reported physical and mental health, depression) and added

numeracy as another important variable to consider. Numeracy can be protective if high; 

but serves as a risk factor if low. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
6

In our second empirical manuscript, we further developed this theme with an 

emphasis on the impact of social network size, perceived social support, and 

positive/negative interaction with loved ones on FE. Additionally, risk factors frequently 

cited in the literature among community-dwelling samples, including demographics, 

dependency, depression, physical functioning, and cognitive functioning were also 

included in the study as control variables. Since past FE studies did not investigate the 

effect of older adults’ “close others” on their FE experience, and rarely incorporated all 

risk factors in a single study, a comprehensive investigation of the relative strength of 

risk factors has been lacking. It was hypothesized that, social network size may not relate 

to FE because bivariate correlations were not significant, but perceived social support and 

positive/negative interaction with close others would be associated with FE incidence, 

even in the presence of other known risk factors. 

Five hierarchical linear regression models were built to examine which social 

factors were predictive of FE. Model 1 was the baseline model including demographic 

variables (age, gender, ethnicity, marital status) and known risk factors (dependency, 

physical health, depression, cognition). Demographic variables were entered first, then 

known risk factors were entered in the second step. Demographic variables explained a 

significant proportion of variance in the OAFEM, R2 = .06, F(4, 353) = 5.44, p < .001, 

and known risk factors explained the variances above and beyond demographics, ΔR

2 

= .08, F(4, 349) = 8.21, p < .001. In the final model, gender (being a male, b = .23, SE 

= .06), worse physical health (b = -.01, SE < .01), and depression (b = .01, SE < .01) 

predicted FE in the presence of other variables (see Table 2, Model 1) similar to previous 

research. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
7

Next, social factors were added to build separate models in testing their effects in 

explaining FE above and beyond demographics and known risk factors. Model 2 added 

subjective measures in the third step, including perceived social support and loneliness. 

Demographics and known risk factors continued to explain FE variances, but perceived 

social support and loneliness did not add much (see Table 2, Model 2). 

Model 3 added objective measures of social factors in the third step, including the 

three SNI indices and social participation. The sample size was smaller, because a lot of 

participants did not complete the SNI. Demographics continued to explain FE variances, 

R

2 = .06, F(4, 214) = 3.37, p = .011, so did known risk factors above and beyond 

demographics, ΔR

2 = .08, F(4, 210) = 4.57, p = .001. However, objective measures of 

social factors did not contribute above and beyond known risk factors (see Table 2, 

Model 3). 

Model 4 tested participants’ social exchanges with close others, including both 

positive and negative exchanges, above and beyond demographics and known risk 

factors. Both demographics and known risk factors explained significant FE variances 

when entered in step 1 and 2; most importantly, participants’ social exchange contributed 

in explaining the variances above and beyond, ΔR

2 = .02, F(2, 340) = 3.38, p = .035. 

Controlling for all predictors, gender (being a male, b = .26, SE = .06), depression (b 

= .01, SE < .01), and negative exchanges with close others (b = .10, SE = .05) predicted 

FE in the final model (see Table 2, Model 4). 

The last model further examined the effects of negative exchanges with close 

others as a risk factor. The last hierarchical regression analysis included all social factors 

in addition to demographics and known risk factors. Demographic variables were entered 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
8

in the first step, known risk factors were entered in the second step, subjective social 

measures were entered in the third step, objective social measures were entered in the 

fourth step, and exchanges with close others was entered in the last step. Exchanges with 

close others explained FE variances above and beyond variables entered in previous 

steps, ΔR

2 = .04, F(2, 197) = 5.05, p = .007. Worse physical health, depression, lower 

level of perceived social support, loneliness, and negative exchanges with close others 

were significantly associated with the OAFEM in bivariate correlation, but only gender 

(being male, b = .19, SE = .08) and negative exchanges with close others (b = .13, SE 

= .06) stayed as predictors of FE in the final model controlling for other variables (see 

Table 2, Model 5). 

Overall, we found that negative interactions with close others was the only 

significant social factors that predicted FE. Perceived social support, network size, and 

positive interactions with close others were not were not associated with FE when known 

risk factors were included in the model. Additionally, among the risk factors, poor 

physical health and depression were related to FE in some models, but not the final model 

when all factors were included. Being a male was another predictor of FE in our sample. 

FE theories guided researchers to investigate four domains of victim vulnerability 

in FE, including dependency, physical, cognitive, and socio-emotional vulnerabilities 

(Conrad et al., n.d.; Kemp & Mosqueda, 2005; Pinsker et al., 2010; Wilber & Reynold, 

1996). Each domain of vulnerability was investigated in previous studies in combination 

with different risk factors, resulting in inconsistent or conflicting findings. Based on the 

theoretical models, we further reviewed and selected risk factors in the FE literature, and 

aimed to integrate all and identify the stronger predictors of FE risk in our second paper.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
9

With the findings that negative interaction with loved ones being the strongest predictor 

of FE, older adults’ socio-emotional vulnerability was the domain that merits the 

attention of prevention and intervention works, with the focus on those interpersonal 

relationships that matter most to older adults. Since older adults value the pursuit of 

emotional goals over other priorities in life according to Carstensen’s socioemotional 

selectivity theory (Carstensen, Fung, & Charles, 2003), the quality of interpersonal 

interactions with beloved family and friends can be especially essential factors in FE 

cases. In light of the statistics informing us perpetrators are mostly likely to be family 

members or trusted others (Schafer & Koltai, 2015), negative interactions among family 

members can be a warning sign of vulnerability. In situations where loved ones are not 

perpetrators, negative interaction can operate in ways that either lowers older adult’s 

perceived social support, or creates a vulnerable environment where perpetrators are 

more likely to target older adults as FE victims due to the lack of emotionally meaningful 

ties. A recent study that used experimental methods and demonstrated the personal 

experience of social exclusion can lead to risky financial decision-making (Duclos, Wan, 

& Jiang, 2013). Because certain types of FE, such as investment fraud, would not take 

place without risky financial decision-making, not feeling included with close others 

could be a risk factor. 

In addition to the novel risk and protective factors identified in the two papers, 

examining across the papers, the risk factors selected from the literature played a role in 

predicting FE, too. Physical and mental health continued to predict FE in the final model 

in the first paper. Though not significant in the final model of our second paper, physical 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
10

health and depression were also significant predictors in the initial model, indicating the 

importance of including them in prevention and intervention program development. 

Implication for Criminal Justice Policy:

FE is a serious and growing social problem. Our work finds evidence of mild risk 

even in high functioning community members. Overall, our preliminary findings 

indicated that community samples look very different from forensic center samples, and 

therefore models of vulnerability are also likely different. We see numeracy can behave 

as both a risk factor when it is low, or a protective factor when high. 

It is essential to start thinking about the social interaction that carries weight in 

older adults’ lives when we work to design prevention and intervention programs for FE 

clients. As FE cases can be highly specific from one to another, designing prevention or 

intervention around reducing negative exchanges with close others might not be as easy, 

but based on our data, it is certainly a more effectively way to reduce FE risk. It is 

important to mention that poor physical health and depression were also predictors of FE 

in some models; therefore, incorporating ways to enhance both physical and mental 

health might be a universal approach to reduce FE risk when a client-centered approach is 

not feasible. For instance, a meta-analysis on loneliness interventions (Masi, Chen, 

Hawkley, & Cacioppo, 2011) revealed that encouraging participants to challenge their 

own negative thought processes on perceived social isolation, which can happen to 

depressed individuals, was more effective than interventions seeking to enhance social 

support or increase opportunities for social contact. The same principles should be 

applied to establishing FE prevention and intervention programs.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
11

We hope that this research will offer criminal justice policy-makers baseline 

information on how common different types of offenses are in a representative sample. 

Further, this data will allow legislators to better allocate criminal justice resources so that 

they more accurately target FE rates and different types of FE crimes in California as 

well as provide estimates of rates of FE more broadly. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
12

References 

Carstensen, L. L., Fung, H., & Charles, S. (2003). Socioemotional selectivity theory and 

the regulation of emotion in the second half of life. Motivatino and Emotion, 27(2), 

103–123. http://doi.org/10.1023/A:1024569803230 

Conrad, K. J., Iris, M., Ridings, J. W., Langley, K., & Wilber, K. H. (2010). Self-report 

measure of financial exploitation of older adults. The Gerontologist, 50(6), 758–773. 

http://doi.org/10.1093/geront/gnq054 

Conrad, K. J., Liu, P.-J., Beach, S. R., & Iris, M. (n.d.). Testing a theory of elder financial 

exploitation using Adult Protective Services data. Journal of Interpersonal Violence. 

Duclos, R., Wan, E. W., & Jiang, Y. (2013). Show me the honey! Effects of social 

exclusion on financial risk-taking. Journal of Consumer Research, 40(1), 122–135. 

http://doi.org/10.1086/668900 

Hung, A., Parker, A. M., & Yoong, J. (2009). Defining and measuring financial literacy. 

SSRN Electronic Journal, 708. http://doi.org/10.2139/ssrn.1498674 

Kemp, B. J., & Mosqueda, L. A. (2005). Elder finanical abuse: An evaluation framework 

and supporting evidence. Journal of the American Geriatrics Society, 53(7), 1123–

1127. http://doi.org/10.11 n/j.l532-5415.2005.53353.x 

Masi, C. M., Chen, H.-Y., Hawkley, L. C., & Cacioppo, J. T. (2011). A meta-analysis of 

interventions to reduce loneliness. Personality and Social Psychology Review, 15(3), 

219–266. http://doi.org/10.1177/1088868310377394 

Pinsker, D. M., McFarland, K., & Pachana, N. a. (2010). Exploitation in older adults: 

Social vulnerability and personal competence factors. Journal of Applied 

Gerontology, 29, 740–761. http://doi.org/10.1177/0733464809346559 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
13

Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy 

influences risk comprehension and medical decision making. Psychological Bulletin, 

135(6), 943–973. http://doi.org/10.1037/a0017327 

Schafer, M. H., & Koltai, J. (2015). Does embeddedness protect? Personal network 

density and vulnerability to mistreatment among older american adults. The 

Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 

70(4), 597–606. http://doi.org/10.1093/geronb/gbu071 

Wilber, K. H., & Reynold, S. L. (1996). Introducing a framework for defining financial 

abuse of the elderly. Journal of Elder Abuse & Neglect, 8(2), 61–80.

Wood, S. A., Liu, P.-J., Hanoch, Y., & Estevez-Cores, S. (2015). Importance of 

numeracy as a risk factor for elder financial exploitation in a community sample. 

The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 

1–7. http://doi.org/10.1093/geronb/gbv041 

Wood, S. A., Rakela, B., Liu, P.-J., Navarro, A. E., Bernatz, S., Wilber, K. H., Allen, R., 

& Homier, D. (2014). Neuropsychological profiles of victims of financial elder 

exploitation at the Los Angeles County Elder Abuse Forensic Center. Journal of 

Elder Abuse & Neglect, 26(4), 414–423. 

http://doi.org/10.1080/08946566.2014.881270 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
14

Table 1 

Demographic Information (N =395) 

Variables M (SD); N (%)

Age 73.3 ( 7.9)

Gender Male 106 (26.8)

Female 280 (70.9)

Missing 9 ( 2.3)

Ethnicity White 275 (69.6)

Hispanic/Latino 42 (10.6)

African American 28 ( 7.1)

Asian 16 ( 4.1)

American Indian 4 ( 1.0)

Mixed Ethnicity 7 ( 1.8)

Other 5 ( 1.3)

Missing 18 ( 4.6)

Education Levels Less than high school 15 ( 3.8)

High school/GED 44 (11.1)

Vocational certification 7 ( 1.8)

Some college 110 (27.8)

Associate’s degree 36 ( 9.1)

Bachelor’s degree 79 (20.0)

Master’s degree 71 (18.0)

Doctoral degree 9 ( 2.3)

Professional doctorate 13 ( 3.3)

Missing 11 ( 2.8)

Speaking English at Home Yes 359 (90.9)

No 21 ( 5.3)

Missing 15 ( 3.8)

Marital Status Married 133 (33.7)

Separated/Divorced 116 (29.4)

Widowed 96 (24.3)

Single 27 ( 6.8)

Cohabiting 10 ( 2.5)

Missing 13 ( 3.3)

Standard of Living Below average 74 (18.7)

Average 214 (54.2)

Above average 95 (24.1)

Missing 12 ( 3.0)

Income Level Under $15,000 56 (14.2)

$15,001-$30,000 103 (26.1)

$30,001-$45,000 62 (15.7)

$45,001-$60,000 47 (11.9)

$60,001-$75,000 27 ( 6.8)

$75,001-$100,000 34 ( 8.6)

Over $100,000 32 ( 8.1)

Missing 34 ( 8.6)

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15

Number of People Depending on the Income 1 (alone) 188 (47.6)

2 157 (39.7)

3 14 ( 3.5)

4 12 ( 3.0)

5 3 ( .8)

6 1 ( .3)

Missing 20 ( 5.1)

Number of People Living together 1 172 (43.5)

2 163 (41.3)

3 14 ( 3.5)

4 13 ( 3.3)

5 4 ( 1.0)

6 1 ( .3)

Missing 28 ( 7.1)

Mini-Mental State Examination 30 130 (32.9)

29 87 (22.0)

28 65 (16.5)

27 44 (11.1)

26 19 ( 4.8)

25 16 ( 4.1)

24 11 ( 2.8)

23 5 ( 1.3)

22 7 ( 1.8)

20 1 ( .3)

Missing 10 ( 2.5)

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
16

Table 2

Linear Regression Predicting the OAFEM with Demographic Predictors, Known Risk Factors of FE, and Social Factors

Model 1 (n = 358) Model 2 (n = 352) Model 3 (n = 219) Model 4 (n = 351) Model 5 (n = 214)

Variables β p β p β p β p β p

Age -.089 .109 -.071 .211 -.183 .014 -.061 .276 -.135 .067

Gender .203 <.001 .208 <.001 .175 .016 .230 <.001 .174 .017

Ethnicity -.011 .839 -.011 .837 .017 .807 -.009 .858 .035 .621

Marital Status .027 .611 .030 .570 .003 .969 -.003 .951 -.023 .774

Dependency -.032 .559 -.029 .734 -.036 .616 -.019 .730 .012 .868

Physical Health -.112 .048 -.121 .033 -.098 .194 -.094 .098 -.084 .266

Depression .226 <.001 .177 .008 .255 .001 .204 .001 .127 .149

Cognition .028 .613 .009 .866 .028 .700 -.001 .991 -.025 .735

PSS .072 .333 -.072 .461

Loneliness .128 .106 .163 .126

ND .072 .479 .038 .708

# SN .096 .374 .073 .493

# EN -.017 .878 -.006 .956

SP .040 .597 .071 .376

Negative .116 .029 .150 .029

Positive .059 .270 .143 .055

Note. PSS = Perceived Social Support; ND = Network Diversity; # SN = Number of People in Social Network; # EN = Number of 

Embedded Network; SP = Social Participation; Negative = Negative Social Exchanges with Close Others; Positive = Positive Social 

Exchanges with Close Others; OAFEM = Older Adult Financial Exploitation Measure. Gender is coded as Male = 1, Female = 0. 

Ethnicity is coded as White = 1, Minority = 0. Marital status is coded as 1 = Married, 0 = Others.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.