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Module 1 Slide deck: Child Welfare Data 101

Module 1 Slide deck: Child Welfare Data 101. Instructor Notes about this module. This module serves as an entry point for the entire course, it’s purpose is to: Help reduce student anxiety and resistance towards engaging in research and completing the course

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Module 1 Slide deck: Child Welfare Data 101

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  1. Module 1 Slide deck:Child Welfare Data 101

  2. Instructor Notes about this module • This module serves as an entry point for the entire course, it’s purpose is to: • Help reduce student anxiety and resistance towards engaging in research and completing the course • Provide an overview of California's child welfare data • Introduce basic statistical concepts that are informative, accessible, and relatively easy to generate • Increase students’ comfort level with data, thereby instilling confidence in their capacity as informed consumers of data and research Module 1: Child Welfare Data 101

  3. Agenda • Introduction and purpose/goals of the course • Data basics: • Overview of sources and uses of data • Sampled vs. population-level data • Aggregate vs. micro-level data • Longitudinal vs. cross-sectional data • Introduction to accessing and using California’s child welfare data • Understanding and computing basic descriptive statistics with child welfare data (e.g. variable descriptions, measures of central tendency, computing percentages) Module 1: Child Welfare Data 101

  4. Opening Slides Engaging Students in Research, possible slides for instructor use to introduce course

  5. The Problem • MSW students rank research courses as one of their least favorite classes in the program (anecdotal…) • Coursework feels disconnected from practice • Most students do not enter with strong math or stats backgrounds – high anxiety • Timeline allows only a superficial coverage of analytic methods • Need to develop “statistical literacy” • Knowledge which enables people to think for themselves, judge independently, and discriminate between good and bad information (Dewey, 1930) Module 1: Child Welfare Data 101

  6. Competing Course Models technology = data are everywhere! field is increasingly oriented around continuous improvement, outcomes statistical literacy & critical thinking necessary for EBP and EIP difficult (if not impossible) to do well even successful training is lost as not used in post-grad work burdens agencies tasked with helping students access data Module 1: Child Welfare Data 101

  7. Why we must be skilled consumers of research… • Ethical obligation to our clients to be up to date on the most recent research and skilled in its critical appraisal • Cannot only rely on researchers to tell us the relevant results and findings • Necessary for effective advocacy and practice • Helps with the efficient translation of research to practice • Critical that practitioners/researchers can speak the same “language” in order to ensure future research efforts are relevant and that findings are understood in context and translated appropriately Module 1: Child Welfare Data 101

  8. Managing by Data Provides social workers with the ability to: Compare metrics with agency mission and practice model Connect to evidence-based practice and desired outcomes Strategize on what work needs to be done Focus on what is being achieved Identify what needs attention Module 1: Child Welfare Data 101

  9. Connecting Data to Practice HYPOTHESIS: A HIGH LEVEL CAUSE AND EFFECT STATEMENT IN OTHER WORDS… Module 1: Child Welfare Data 101 Slide Developed by NY OCFS

  10. This Course One of the most important you will take here at X(in my humble opinion) Focuses on practical skills, understanding data, statistical literacy, consuming research Very connected to your current field placement – and work post-graduation – yet helps you acquire empirical skills you may not otherwise have the opportunity to develop outside of your graduate studies Expects that you read, ask questions, think critically, and engage with the material Requires that you produce a relevant, readable, empirical research report based on publicly available administrative (secondary) child welfare data

  11. Administrative Data Collected during the normal course of agency operations Tabulated/aggregate data are publicly available Full coverage of populations served Free of “reactivity”(data problems are usually transparent) Analysis of trends over time Performance indicators Social indicators Particularly salient to social work… Module 1: Child Welfare Data 101

  12. Why a Secondary Analysis of Administrative Data? • Data directly support agencies and capture information for the clients we are working with • Increasing emphasis on making these data available to researchers • Technological advances – data storage, frequent refreshes, web-based/online access and analysis tools • Non-intrusive (we work with vulnerable populations and busy co-workers!) • More transferrable to a post-graduate career in social work • Efficient, cheap, available • Useful for advocacy efforts, needs assessment, proposals for new programs (substantiate a service need) • These are needed skills in both public and private agencies Module 1: Child Welfare Data 101

  13. Realities of Administrative Data Analysis Measures are often crude Sometimes limited documentation Missing data Do you trust what has been entered? Often much more difficult to analyze than expected…requires careful thinking May be dated Definitions may have changed over time Module 1: Child Welfare Data 101

  14. Module 1, Section 1 Sources and Uses of Data

  15. Data Sources Census data Longitudinal surveys of a subsample of a population Cross sectional surveys Longitudinal (multi-wave) surveys of a single sample Administrative data Multisource data systems Module 1: Child Welfare Data 101

  16. Uses of Data • Descriptive • Demographic characteristics of a population, place, office, etc. • Trends over time (one period compared to another) • Differences/similarities between groups, counties, placement settings, interventions, etc. • Exploratory • Often conducted as pilot studies, attempt to examine feasibility issues (e.g., recruitment), preliminary data to develop fuller hypotheses and research proposals • Explanatory • Analysis of the relationship between two events (or two variables) • Looking at the contributions of various factors to some outcomes (y=a+bX) • Evaluation • To evaluate social policies, programs, and interventions • The evaluation process encompasses all three uses of data listed above

  17. Data Terminology (Review) • Qualitative vs. Quantitative • why, how vs. who, what, where, when • “All quantitative data is based upon qualitative judgments; and all qualitative data can be described and manipulated numerically.” (Research Methods Knowledge Base) • Longitudinal vs. Cross-Sectional • repeated observations over time vs. a slice • Primary vs. Secondary • you collect it vs. someone else collected it • Aggregate vs. Micro-level • group tabulations vs. individual units • Deidentified vs. Identified • Joe Smith vs. 987334 • Sample vs. Census/Population • partial vs. full coverage Module 1: Child Welfare Data 101

  18. Module1, Section 2 Sample vs. Census/Population

  19. Samples vs. Population population sample • Can we select a few people or things for observation and then apply what we observe to a much larger group? • Often impractical to gather data from the whole population, so samples are drawn (this is not relevant to the administrative data we will be using in this course) • Key is the researcher’s ability to generalize findings from the sample to the whole population • Sample=a finite part of a larger population whose properties are studied to gain information about the whole • If all members of a population were identical in all respects – would we need careful sampling procedures? Module 1: Child Welfare Data 101

  20. The act, process, or technique of selecting a suitable sample, or a representative part of a population for determining the parameters (or characteristics) of the whole population For a sample to provide useful information, it must reflect the same general variations as the overall population NSCAW data = sample; CWS/CMS data = population Sampling population sample Use characteristics/observations of sample, to draw conclusions (inferences) about the larger population Module 1: Child Welfare Data 101

  21. Module 1, Section 3 Aggregate vs. Microdata

  22. Aggregated data

  23. Micro-data (individuals as units of analysis)

  24. Working with Aggregated Data…Disaggregate Aggregate Permanency Outcomes Race/Ethnicity County Age Placement Type One of the most powerful ways to work with data… Disaggregation involves dismantling or separating out groups within a population to better understand the dynamics Useful for identifying critical issues that were previously undetected Module 1: Child Welfare Data 101

  25. The Problem with Summary Statistics: The average human has one breast and one testicle. * * ~Des McHale www.quotegarden.com/statistics.html Module 1: Child Welfare Data 101

  26. 2000 July-December First Entries California: Percent Exited to Permanency 132 Months From Entry, by race and placement

  27. Module 1, Section 4 Longitudinal vs. Cross-Sectional (Point in Time) Views of Data

  28. Time Dynamics • Cross-Sectional Studies • Examines a phenomenon by collecting/examining a “cross-section” of data at one time (one observation at a point in time) • BIG problem: many questions we seek to answer aim to understand causal processes that occur over time (e.g., children in foster care and mental health) • Longitudinal Studies • Based on repeated observations of a given unit over multiple points in time • Trend Studies • Cohort /Panel Studies Module 1: Child Welfare Data 101

  29. Longitudinal Data Longitudinal Analysis Module 1: Child Welfare Data 101

  30. 3 Key Data Views in Child Welfare Module 1: Child Welfare Data 101

  31. The “View” Matters! January 1, 2005 July 1, 2005 January 1, 2006 Child 1 Child 2 Child 3 Child 4 Child 5 Child 6 Child 7 Child 8 Child 9 Child 10

  32. Longitudinal vs. Point in Time Module 1: Child Welfare Data 101

  33. Module 1: Child Welfare Data 101

  34. Module 1: Child Welfare Data 101

  35. Module 1, Section 5 California’s Child Welfare Data

  36. Why do we have these data? • In 2001, the California Legislature passed the Child Welfare System Improvement and Accountability Act (AB 636) • Designed to improve outcomes for children in the child welfare system while holding county and state agencies accountable for the outcomes achieved • The statewide accountability system went into effect January 1, 2004 • It is an enhanced version of the federal oversight system mandated by Congress to monitor states’ performance and provides the legal framework for the California Child and Family Services Reviews Module 1: Child Welfare Data 101

  37. How are these data used? • The foundation for this new oversight system comes from data obtained from the Child Welfare Services/Case Management System (CWS/CMS) • Each quarter, the state provides county child welfare agencies with county-specific data on 14 outcome measures related to safety, permanency and well-being • The baseline performance data is gathered for each county and also made available to the public • Quarterly reports provide counties with quantitative data and serve as a management tool to track performance over time • Public data is also refreshed quarterly (data available on website is 3-6 months old) Module 1: Child Welfare Data 101

  38. Where are the Data from?

  39. How are the public data configured? personal info unique characteristics and path through the child welfare system age race gender allegation placement disposition county year aggregate data tabulations

  40. What “type” of information is available? Module 1: Child Welfare Data 101

  41. Where can I find these data? http://cssr.berkeley.edu/ucb_childwelfare

  42. Module 1, Section 6 Univariate Statistics, child welfare examples

  43. Variables finite number of values or categories (qualitative in nature) exhaustive mutually exclusive variability in magnitude (quantitative in nature)

  44. Continuous vs. Categorical • The average foster child has 2.6 placements while in foster care • This number makes little sense because the underlying dimension is discrete (i.e., categorical, discontinuous) 1 2 3 4 5 6 2.6 x placements Continuous Data Discrete Data Age Days in Care Percentages / Rates Race/Ethnicity Placement Type Referral Reason Module 1: Child Welfare Data 101

  45. Descriptive Statistics • “Summary” statistics, used to describe what’s “going on” in our data • Describe a situation or condition numerically by quantifying phenomena • In California, there are far fewer children in foster care today than was true a decade ago. • In California, the number of children in foster care today is 47,729 less than was true a decade ago, translating into a 49% decline. • Frequency tables (the distribution), measures of central tendency, measures of variability (the dispersion) Module 1: Child Welfare Data 101

  46. Computing a Percent PERCENT: A proportion in relation to a whole expressed as a fraction of 100. What Percentage of Children reunified in 2005 were reunified within 12 months of entering care? Raw Numbers (counts) # Reunified w/in 12m = 290 = 440 # Reunified (total)

  47. Computing a Rate per 1,000 RATE: A proportion in relation to a whole, can be expressed as a fraction of 100, 1000, 100,000, etc. What was the foster care entry rate in 2005? (i.e., how many children entered care out of all possible children in the population?) Raw Numbers (counts) # Entered Care = 1,333 Scales for a meaningful interpretation… = 363,376 # Child Population

  48. Measures of Central Tendency 12 4 15 63 7 9 4 17 4 4 7 9 12 15 17 63 = 9.7 7 = 9 Mean: the average value for a range of data Median: the value of the middle item when the data are arranged from smallest to largest Mode: the value that occurs most frequently within the data

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