September 18-19, 2006 – Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development Tatjana Meschede, Ph.D., Center for Social Policy, UMass Boston Introduction to Multivariate Analyses

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 2 Why advanced statistics? Advanced statistical analyses can provide answers to questions such as –How multiple characteristics of homeless persons interact and influence outcomes? –How does use of multiple services impact outcomes, and which service type is more influential?

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 3 HUD Required HMIS Data Elements Universal Data Elements Program entry/exit Gender Race/Ethnicity Date of birth to calculate age Disability status Program Level Data Elements Employment/Income/Non- Cash Benefits Education Health/Physical and/or Developmental Disability/HIV/AIDS Veteran status Residence prior to program entry ZIP of last permanent address Mental Health/ Substance Abuse/ Domestic Violence Services Received Reason for Leaving and Destination

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 4 Caveats Data Quality - No Good Answer Without Quality Data! –Data collection – how to ask the questions –Data entry – careful attention to details –Data checking – double checking data for entry errors –Validating data with other data sources –Consistency among programs contributing data All you need to know about HMIS data quality ng_HMIS_Data_Quality.pdf

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 5 Caveats cont. Sample size/Coverage Who does your data represent? –All homeless assistance programs, residential or not, in your CoC? –All agencies with at least one homeless assistance program in your CoC? –All homeless people within your CoC? Who is left out? –People who don’t use shelters –People who use particular types of shelters (e.g.. DV; 12-hour; missions) –People who don’t want to provide information to the HMIS database

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 6 Types of Analyses - Definitions Descriptive/Exploratory/Explanatory –Descriptive: Presenting characteristics of people accessing homeless services in your CoC (e.g. 28% Female) –Exploratory: Analyze difference of homeless sub- populations, for example long-term vs. short-term users of services (e.g. Females are more likely to be more short-term shelter users) –Explanatory: Predict relationships between variables/testing more complex analytic models (Female were more likely to be short-term shelter users because they received more services) Cross-sectional/Longitudinal –Cross-sectional: Point in time –Longitudinal: Over time

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 7 Descriptive Statistics: Summarize and Describe Information For Example, Average Age of MA homeless shelter users = Marital Status of MA homeless shelter users FrequencyPercent Single848163% Married/Partnered10248% Divorced264020% Separated10528% Widowed2912% Total %

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 8 Inferential Statistics: Can I infer from my sample to the larger homeless population? For Example, comparing # of shelter nights for homeless people with a high school degree or more to those of lesser education attainment

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 9 Bivariate Statistics: Relation between just two variables For Example, the correlation between number of nights spent at a homeless shelter and moving into permanent housing

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 10 Multivariate Statistics to assess relationships of 2 or more variables Important concepts: Significance-Can the findings be generalized to a broader universe? Strength of the relationship between variables-is the relationship weak or strong?

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 11 Multivariate Analytic Model Demographic Characteristics Gender Race (white/non-white) Age Service Readiness Health Insurance (yes/no) Psychiatric Disability (yes/no) Substance Abuse yes/no) Months as BHCHP Patient Medical and Substance Abuse Service Use Admission to Respite Care (yes/no) Length of Stay at Respite Admission to BMC ER (yes/no) Admission to BMC Inpatient (yes/no) Admission to Detoxification (yes/no) Outcomes Housed or in Long-Term Program Remaining High-Risk on the Streets Death

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 12 Conditions for Regression Analyses Levels of measurement Causal order Limitations/Assumptions (David)

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 13 Levels of Measurement Nominal (characteristics, no ranking) –For Example: Race 1 = American Indian/Alaska Native 2 = Asian, 3 = Black or African Am. 4 = Native Hawaiian or Other Pacific Islander 5 = White Ordinal (categorize and ranking) –For Example: 1 = Grade School 2 = Some High School 3 = High School Diploma/GED 4 = Some College 5 = College Degree Interval/Ratio –For Example Age

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 14 Causal Order The Independent Variables (IVs) occur before the Dependent Variable (DV). Causal order using cross-sectional data can only be determined after extensive model building and testing. –E.g. Which comes first: Homelessness or Mental Health Issues?

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 15 Limitations and Assumptions Variables are measured at the interval level –but often ordinal variables with a large number of values are included as well. Converting nominal variables into dichotomous variables for regression analyses is acceptable (e.g. for race: white/non-white).

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 16 Predicting # of Shelter Nights Using 2003 MA HMIS data Gender (Male) Race (White) Age Marital Status (divorce/separated/widowed) Education (HS +) Number of Nights Spent in a Homeless Shelter

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 17 Steps to Interpret the Statistical Output Is it a good model (Adjusted R Square.5 or higher)

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 18 Steps to Interpret the Statistical Output Is it a significant model (Sig. below.05) – i.e. Can you apply these findings to a broader universe, or are the findings just due to chance?

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 19 Steps to Interpret the Statistical Output Which are the important predictors of # of shelter nights (Sig. below.05 and Beta 2 or higher)

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 20 Additional Questions What analyses should you conduct? What software to use for advanced statistical analyses? Who should conduct advanced statistical analyses?

September 18-19, Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development 21 Limitations of HMIS Data Analyses Required data elements limiting the scope of the analyses Linkage to other administrative data sets that include records on people accessing homeless services Other forms of data collection to compliment HMIS data analyses and reporting