Summer 2026 SOWK 588 Week 04 - Applying Logical Reasoning to a Social Policy

Summer 2026 SOWK 588 Week 04 - Applying Logical Reasoning to a Social Policy

title: Summer 2026 SOWK 588 Week 04 - Applying Logical Reasoning to a Social Policy date: 2026-06-15 23:12:33 location: Heritage University tags:

• Heritage University
• MSW Program
• SOWK 588 presentation_video: > “” description: >

Week four is asynchronous. In Edin and Shaefer (2016), students will read about the impact of poverty on housing. Students read two chapters in Linquiti (2022), focusing on logical reasoning and how we collect and evaluate evidence in policy analysis. There are forums where students can reflect on housing challenges, engage with the textbook’s end-of-chapter discussion questions, practice applying reasoning methods, begin developing the evidence base for their policy analysis papers, and examine the distinction between advocacy and neutral policy analysis. I will have a lecture video talking about logical reasoning and how it can be applied to policy analysis. The agenda includes:

• Week four tasks
• Applying logical reasoning to a social policy

The learning objectives this week include:

• Distinguish between deductive, inductive, and abductive reasoning and identify how each applies to social policy analysis
• Examine the impact of housing instability on individuals and families living in extreme poverty and connect those challenges to policy-level gaps and responses
• Describe the distinction between policy advocates and policy analysis professionals and reflect on its implications for social workers who occupy both roles simultaneously
• Evaluate the strength and appropriate use of different types of evidence in the construction of policy arguments.
• Begin identifying and organizing evidence for the policy analysis paper topic.

Plan for Week Four

Agenda

• Week four tasks
• Applying logical reasoning to a social policy

Learning Objective

Distinguish between deductive, inductive, and abductive reasoning and identify how each applies to social policy analysis

Week Four Tasks

Content

• Read Edin and Shafer (2016) Chapter 3: A Room of One’s Own
• Read Linquiti (2022) Chapter 5: Using Logic to Identify Tentative Truths
• Read Linquiti (2022) Chapter 6: Collecting and Evaluating Evidence for Use in Policy Analysis
• Watch my lecture video

W-04 A-01 Asynchronous Participation and Engagement

The expectation is that each of your replies will be substantive and provide meaningful perspectives, contributing to the forum’s conversation and scholarship. They can be related to the prompts or building on conversations shared by peers. There are six forums for this week, and you are expected to make at least six replies across any of the forums. These forums include the following:

• Reflection on Challenges of Housing in the $2 per Day Community is a forum where students can reflect on their reading in chapter three and/or share examples of the impact poverty has on housing.
• There are forums for the added discussion questions at the end of each chapter: Chapter Five Discussion Questions and Chapter Six Discussion Questions.
• Reasoning Examples provides a space for students to define methods of reasoning and give an example.
• Students should be working on their papers for this class. Identification of Evidence Related to Your Social Policy Paper is a place for students to share some of their thinking and planning related to this.
• In the forum, Characterization of Advocates versus Policy Analysis Professionals, I ask students to consider the discussion in the textbook and their alignment.

A-03 Take Home Exam 01

You can find A-03 - Take Home Exam 01. It is due Monday, 6/22 at 8 am.

Policy Example - HUD’s Housing Choice Voucher Program

This week’s reading covers five reasoning types policy analysts use to build “tentative truths.” Today we apply three of them — deductive, inductive, abductive. I want to go through the steps of applying each of them to a policy to demonstrate what it looks like. It made sense to use the example of Section 8 housing (i.e., the HUD Housing Choice Voucher Program), as that was also discussed in Edin and Shaefer’s (2016) reading this week. So we will look at the same policy, but through three different reasoning lenses, three different kinds of conclusions.

How can we use Section 8 Housing to go through these logic examples.

The Housing Choice Voucher program, commonly called Section 8, takes its name from Section 8 of the Housing Act of 1937, which is codified at 42 U.S.C. § 1437f(o). The statute directs the Secretary of Housing and Urban Development to make assistance payments to private landlords on behalf of eligible low-income families, allowing participants to choose their own rental housing in the private market rather than being placed in government-owned units.

(United States Housing Act of 1937, 42 U.S.C. § 1437f(o), 2024)

• The nation’s largest federal rental assistance program
• Subsidizes the rents of more than 2.3 million households (5 million people)
• Administered locally by roughly 2,100 public housing authorities

(McCarty, 2023)

Edin and Shafer (2016):

• Describe a random-controlled-trial evidence that shows vouchers considerably reduce housing instability: they cut the chances of homelessness (doubled-up or on the street), halve the share of families in overcrowded units, and sharply reduce how often a family has to move.

Only about a quarter of income-eligible families receive any kind of rental subsidy, and that share has shrunk since the 1980s. Waiting lists are long and sometimes closed outright: 85,000 families in Chicago’s queue, 268,000 in New York City’s. Jennifer Hernandez, profiled in the chapter, never received a housing subsidy despite three separate spells of $2-a-day poverty — she simply couldn’t get on the list.

The Five Reasoning Types

Linquiti (2022) identifies five distinct types of logical reasoning used in policy analysis:

• Deductive reasoning: Multistep reasoning that opens with a general principle, layers in a specific case, and derives a conclusion from the two. Its classic structure is the syllogism. What sets it apart is certainty — when the premises are true and the logic holds, the conclusion necessarily follows, which no other mode of reasoning can promise.
• Causal reasoning: A specialized form of deduction that posits a mechanism linking a cause to an effect. A sound causal claim has to satisfy four conditions: the cause precedes the effect in time, the two vary together consistently, competing explanations are ruled out, and—ideally—a theory explains why the relationship exists.
• Inductive reasoning: The reverse of deduction. Rather than running from general rules down to specific cases, it begins with observations of particular instances and tries to infer the principles, patterns, and mechanisms that account for them. As a result, its conclusions are always tentative—we never prove them outright, we only fail to falsify them. It comes in two forms: reasoning from examples (inferring from observed cases) and reasoning from analogy (assuming a trait of one case carries over to a comparable one).
• Abductive reasoning: An iterative loop that begins by examining the available evidence, generates educated guesses about hypotheses that might explain it, uses deduction to predict what each hypothesis would produce, weighs those predictions against what’s actually observed, and lands on whichever hypothesis best accounts for the evidence. It’s also called inference to the best explanation.
• Reasoning by classification (§5.5): An inductive method for taming large amounts of information by sorting similar items into groups. The categories should be collectively exhaustive (everything has a place) and mutually exclusive (nothing fits in two places at once). How many categories you need, and how you define them, depends on the purpose of the analysis.
• Probabilistic reasoning: Handles uncertainty by assigning likelihoods to tentative conclusions—either as numeric probabilities or as estimative words like likely, unlikely, or very likely. It also requires keeping the base rate in view; ignoring it (base rate neglect) puts the validity of the conclusion at risk.

(Linquiti, 2022)

We are going to talk about deductive, inductive, and abductive

Deductive Reasoning

Deductive reasoning works well when the rules are hard and legally fixed. The statute and HUD regulations create clean syllogisms:

• Premise 1: Under 42 U.S.C. § 1437f(o), a family is income-eligible for a Housing Choice Voucher only if their income does not exceed 50% of the area median income (AMI) for their location.
• Premise 2: A family of four in Yakima, WA earns income at 48% of the local AMI.
• Conclusion: This family meets the federal income eligibility threshold for a Housing Choice Voucher.

This is a sound argument — the rule is real, the situation satisfies it, the conclusion holds.

Where it can go wrong — a common policy rhetoric mistake:

• Premise 1: If a family receives a Housing Choice Voucher, they can afford stable housing.
• Premise 2: This family has a voucher.
• “Conclusion”: Therefore this family has stable housing.

The logic is valid (affirming the antecedent), but Premise 1 is false — vouchers don’t guarantee stable housing if landlords refuse to accept them or if no units are available at the payment standard. A sound syllogism requires valid logic and true premises; here we have one but not the other.

Inductive Reasoning

Inductive reasoning by example draws from repeated observations toward a broader generalization:

We could make the following inductive claims:

• Landlord refusal of vouchers is a widespread, structural barrier.
• Voucher holders face the most rejections precisely in the neighborhoods that would get them out of high-poverty settings.

There is a report commissioned by HUD by Cunningham et al. (2018) that tested landlord acceptance of vouchers across five states. The denial rates widley varied, but we do see it is significantly high in 4 of the 5 and we see there is a consistent difference in 4 of the 5 based on the area where the request is made (much more likely to be denied in a low or medium poverty areas).

(Cunningham et al., 2018)

Following trends like this repeated pattern across five distinct housing markets is how we can inductively generalize. Also, remember that this is inductive, not deductive. That means it is a probable generalization, not a certainty. The variation across sites also shows that the pattern isn’t uniform: cities with source-of-income antidiscrimination laws (Newark and DC) had far lower denial rates, which means the generalization doesn’t hold equally everywhere and points toward a policy lever.

Abductive Reasoning

Abductive reasoning starts not with a principle or a pattern, but with a puzzle — an observed outcome that doesn’t match expectations and demands an explanation.

Chetty et al. (2016) used data from a randomized federal experiment (the Moving to Opportunity project) and researchers found that children whose families used vouchers to move to lower-poverty neighborhoods before age 13 had substantially better long-term outcomes — higher earnings, higher college attendance, and lower rates of single parenthood for daughters.

We know that many voucher holders remain concentrated in high-poverty neighborhoods. We can consider an Analysis of Competing Hypotheses (ACH). Rather than starting with a preferred explanation and looking for confirming evidence, ACH forces the analyst to (1) generate all plausible hypotheses upfront, (2) list the evidence relevant to each, (3) evaluate how consistent each piece of evidence is with each hypothesis, and (4) favor the hypothesis (or combination of hypotheses) that best accounts for all the evidence while being least inconsistent with any of it. It is a disciplined way of avoiding confirmation bias in the explanation-building process.

Competing hypotheses (ACH-style):

1. Payment standard limits — the maximum rent a voucher covers may not reach market-rate units in lower-poverty, higher-opportunity areas, making the voucher functionally unusable there regardless of family intent.
2. Landlord discrimination — in most states it is legal for landlords to refuse vouchers; owners in higher-opportunity areas often do.
3. Information and social network barriers — families may lack connections to higher-opportunity areas, have no prior experience navigating those housing markets, or face administrative burdens in porting their voucher across jurisdictions.
4. Constrained search windows — HUD rules give families a limited time window to find housing after receiving a voucher; that clock disadvantages families trying to access tighter, more competitive markets.

We would have to look at the evidence of each of these. This is likely a multi-cause story. An iterative abductive process would look at each hypothesis against local data before recommending a policy response — and note that different cities may have different dominant causes.