Characteristics and Dynamics of Homeless Families with Children. Predicting Residential Stability and Homelessness

10/01/2007

A second set of analyses were performed to answer the questions:

  • What are the risk and protective factors that differentiate homeless families from all others?
  • What are the risk and protective factors that differentiate residentially stable families from all others?

To answer these questions, statistical procedures (logistic regressions) were used that could test for the effects of all relevant variables at one time (rather than one at a time, as in the descriptive analyses). By looking at all variables simultaneously, it is possible to identify variables that are relatively more important in distinguishing residentially stable families from all others or those that are relatively more important in distinguishing homeless families from all others. The variables that set residentially stable families apart from others may be considered "protective" factors for homelessness and residential risk, while the factors that distinguish homeless families from all others can be considered potential "risk" factors for homelessness.

Logistic regressions were computed for Year 1 groups, Year 3 groups, and the combined residential groups. Only those variables that showed substantial variation between housing groups (e.g., there were statistically significant differences between stably housed or homeless and at least two of the three other groups) or were considered important background and demographic characteristics were included in these analyses. Each logistic model began by entering all of the variables in the model, and then removing non-significant variables4. Tables 5-4 and 5-5, which show the results from these logistic analyses, list all of the variables that were initially included in the model (e.g., non-shaded variables), but parameter estimates are only shown for those variables that were statistically significant at the .05 level in the final models.

Three models examined the factors that related to a family experiencing recent homelessness at Year 1, Year 3, and at either time-point. Three additional models examined the factors that related to a family remaining residentially stable at Year 1, Year 3, and at both time-points.

Homelessness. Table 5-3 presents the results of the three homeless models (Year 1, Year 1 and 3, and Year 3). The Nagelkerke R2 (a pseudo- R2 statistic that measures the amount of variance explained by the model) for the Year 1 and Year 1-3 models are both less than .2, indicating that neither model is doing a very good job of fitting the data. The Year 3 model has a Nagelkerke R2 of .333, however, indicating that this is a better fitting, more powerful model (closer to Cohen's definition of a medium effect).

Only one variable, income, is significant in all three models. Families with relatively higher household incomes were consistently less likely to experience homelessness, an effect that was strongest for the Year 3 model (parameter estimate of -.303).

A few variables were significant in two of the three models. Receiving housing assistance (local, state, or Federal) appears to be a protective factor. People who reported receiving housing assistance at baseline or Year 1, as well as those who obtained housing assistance during the followup period, (having a negative coefficient for the change score) were also less likely to experience homelessness.

Mental health issues, substance abuse issues, and reports of domestic violence were also somewhat related to a greater likelihood of experiencing homelessness. Finally, receipt of TANF was positively related to the likelihood of becoming homeless, but was likely a proxy for need and lack of income rather than a predictor of homelessness.

  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-3.5
Logistic regression models year 1 and year 3 homeless households
at least 50 percent below poverty line
Nagelkerke R2 n=778 n=775 n=688
.157 .166 .333
Age      
Race (% Black)      
Live with both parents @ 15      
Teen Birth     .872*
Pregnant @ Year 1      
Pregnant @ Year 3      
Partner – Baseline      
Partner – Yr 1      
Change partner B-1      
Change partner 1-3     -1.536***
Live with mother – Base      
Live with mother – Yr 1     1.007*
Change live Mom B-1      
Change live Mom 1-3      
Number adults in household – Base      
Number adults in household – Yr 1      
Number adults in household – Yr 3     .509**
Number of children – Baseline      
Number of children – Yr 1      
Number of children – Yr 3      
Social Support – Base
(# Sources 0-3)
     
Social Support – Yr 1      
Social Support – Yr 3      
$1,000 Loan – Yr 1      
$1,000 Loan – Yr 3     -1.303*
Education – Baseline (<HS/HS+)      
Mother working – Base      
Mother working – Yr 1     -1.537*
Change Mom work B-1      
Change Mom work 1-3     -1.803**
  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-3.
Logistic regression models year 1 and year 3 homeless households at least 50 percent below poverty line (continued)
Nagelkerke R2 n=778 n=775 n=688
.157 .166 .333
Income – Year 1 (ln) -.155* -.182** -.303***
Partner working – Base      
Partner working – Yr 1      
Change partner work B-1      
Change partner work 1-3      
Other adult work – Base      
Other adult work – Yr 1      
Other adult work – Yr 3      
Health status – Base
(1:Excellent to 5:Poor)
     
Health status – Yr 1      
Health status – Yr 3      
Ever use SA – Base and Yr 1 1.076*    
SA ever interfere – B and Yr 1   .781*  
Ever DV – B and Yr 1 1.092** .764*  
MH Prob – Yr 1 .306 .473***  
Ever use SA – Base, 1, 3      
SA ever interfere – B, 1, 3      
Ever DV – B, 1, 3      
MH Prob – Yr 3     .637**
Neigh Safety – Baseline
(1 Very Safe to 4 Very Unsafe)
    .535*
Public housing – Base      
Public housing – Yr 1      
Change public housing B-1      
Change public housing 1-3      
Housing assistance – Baseline   -.815*  
Housing assistance – Yr 1     -1.473*
Change housing assistance
B-1
-1.029*** -1.359***  
Change housing assistance
1-3
     
TANF/Food Stamps – Base      
Receive TANF – Yr 1 .995** 1.029*** .759
Change TANF 1-3      
Receive food stamps – Yr 1      
Change food stamps 1-3      
* Significant at P<.05
** Significant at P<.01
*** Significant at P<.001

Stably Housed. The descriptive analyses showed that it was often the Stably Housed group that differed the most from the other residential groups. Table 5-4 presents models that examine factors to predict who was residentially stable at Year 1, at Year 3, as well as Year 1 AND Year 3. The overall fit of all three models is fairly consistent and low; Nagelkerke R2 of .221 for the Year 1 model, .183 for the Year 3 model, and .197 for the Year 1-3 model (all would be considered small effects). Table 5-4 presents the results for the stably housed group.

  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-4.
Logistic regression models for year 1 and year 3 stably housed households
at least 50 percent below poverty line
Nagelkerke R2 n=778 n=775 n=688
.221 .197 .183
Age   .033  
Race (% Black)      
Live with both parents @ 15      
Teen birth      
Pregnant @ Year 1      
Pregnant @ Year 3      
Partner – Baseline .530** .548*  
Partner – Yr 1      
Change partner B-1 .456*    
Change partner 1-3     -.303
Live with mother – Baseline      
Live with mother – Yr 1      
Change live Mom B-1 .336    
Change live Mom 1-3     -.479**
Number of adults in household – Baseline .186* .210*  
Number of adults in household – Yr 1      
Number of adults in household – Yr 3      
Number of children – Baseline .194***    
Number of children – Yr 1      
Number of children – Yr 3      
Social Support – Base
(# Sources 0-3)
     
Social Support – Yr 1      
Social Support – Yr 3      
$1,000 Loan – Yr 1 .291    
$1,000 Loan – Yr 3      
Education – Baseline (<HS/HS+)      
Mother working – Baseline -.283    
Mother working – Yr 1      
Change Mom work B-1      
Change Mom work 1-3     .383**
Income – Yr 1 (ln) .091 .112  
Partner working – Base      
Partner working – Yr 1      
Change partner work B-1 .705** .881***  
Change partner work 1-3      
  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-4.
Logistic regression models for year 1 and year 3 stably housed households
at least 50 percent below poverty line (continued)
  n=778 n=775 n=688
Nagelkerke R2 .221 .197 .183
Other adult working –Base      
Other adult working – Yr 1      
Other adult working – Yr 3      
Health status – Base
(1:Excellent to 5:Poor)
-.149 -.323***  
Health status – Yr 1     -.130
Health status – Yr 3      
Ever use SA – Base and Yr1 -.473** -.644**  
SA ever interfere – B and Yr 1      
Ever DV – B and Yr 1 -1.037*** -.928*  
MH Prob – Yr 1 -.546*** -.625***  
Ever use SA – Base, 1, 3     -.692***
SA ever interfere – B, 1, 3      
Ever DV – B, 1, 3      
MH Prob – Yr 3     -.583***
Neigh Safety – Baseline
(1 Very Safe to 4 Very Unsafe)
     
Public housing – Base   .823**  
Public housing – Yr 1     .528**
Change public housing B-1   .548*  
Change public housing 1-3      
Housing assistance – Baseline      
Housing assistance – Yr 1      
Change housing assistance B-1   .352  
Change housing assistance 1-3      
TANF/food stamps – Base      
Receive TANF – Yr 1     -.304
Change TANF 1-3      
Receive food stamps – Yr 1      
Change food stamps 1-3     -.508**
* Significant at P<.05
** Significant at P<.01
*** Significant at P<.001

Looking for results that were significant in more than one model showed that living with a partner/spouse, at least at baseline, made it more likely that a mother would be residentially stable. Changes in this relationship, however, had contradictory effects in different models; in the Year 1 model, having a partner join the household was associated with greater likelihood of being stable, whereas in Year 3, the household was less stable if a partner joined (or more stable if the partner left). Perhaps this was due, in part, to the decrease in partner employment noted earlier in Year 3.

The more adults there are living in the household, and having a spouse/partner who is working or who has found employment, all make it more likely that a mother will be residentially stable. Living in public housing was also frequently associated with being stably housed, while obtaining public housing was significant only for the combined Year 1/Year 3 outcome.

Factors that made it less likely that someone would be residentially stable somewhat mirror the results of the homeless analyses. Reported substance use and mental health issues made it less likely that a woman would be residentially stable in all three models. Poorer reported physical health was also associated with a decreased risk of residential stability in the combined model, and reported domestic violence was significant in two of the models.

View full report

Preview
Download

"report.pdf" (pdf, 4.18Mb)

Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®