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History of logic models

Citing Funnell and Rogers's account (2011),¹³ Joy A. Frechtling's (2015) encyclopedia article¹⁴ traces logic model underpinnings to the 1950s. Patricia J. Rogers's (2005) encyclopedia article¹⁵ instead traces it back to Edward A. Suchman's (1967) book¹⁶ about evaluative research. Both encyclopedia articles and LeCroy (2018)¹⁷ mention increasing interest, usage and publications about the subject.

Uses of the logic model

Program planning

One of the most important uses of the logic model is for program planning. It is suggested to use the logic model to focus on the intended outcomes of a particular program. The guiding questions change from "what is being done?" to "what needs to be done"? McCawley suggests that by using this new reasoning, a logic model for a program can be built by asking the following questions in sequence:

1. What is the current situation that we intend to impact?
2. What will it look like when we achieve the desired situation or outcome?
3. What behaviors need to change for that outcome to be achieved?
4. What knowledge or skills do people need before the behavior will change?
5. What activities need to be performed to cause the necessary learning?
6. What resources will be required to achieve the desired outcome?¹⁸

By placing the focus on ultimate outcomes or results, planners can think backward through the logic model to identify how best to achieve the desired results. Here it helps managers to 'plan with the end in mind', rather than just consider inputs (e.g. budgets, employees) or the tasks that must be done.

Testing the logic of the logic model is an important step in development. If you there is something the program wants to include, how will they do it? That should link to an earlier step in the logic model.¹⁹

Evaluation

The logic model is often used in government or not-for-profit organizations, where the mission and vision are not focused on achieving a financial benefit. Traditionally, government programs were described only regarding their budgets. It is easy to measure the amount of money spent on a program, but this is a poor indicator of outcomes. Likewise it is relatively easy to measure the amount of work done (e.g. number of workers or number of years spent), but the workers may have just been 'spinning their wheels' without getting very far in terms of ultimate results or outcomes.

However, nature of outcomes varies. To measure the progress toward outcomes, some initiatives may require an ad hoc measurement instrument. In addition, in programs such as in education or social programs, outcomes are usually in the long-term and may requires numerous intermediate changes (attitudes, social norm, industry practices, etc.) to advance progressively toward the outcomes.

By making clear the intended outcomes and the causal pathways leading to them, a program logic model provides the basis upon which planners and evaluators can develop a measurement plan and adequate instruments. Instead of only looking at the outcome progress, planners can open the "black box" and examine if the intermediate outcomes progress as planned. In addition, the pathways of numerous outcomes are still largely misunderstood due their complexity, their unpredictability and lack of scientific / practical evidences. Therefore, with proper research design, one may not only assess the progress of intermediate outcomes, but evaluate as well if the program theory of change is accurate, i.e. is successful change of an intermediate outcomes provokes the hypothesized subsequent effects in the causal pathway. Finally, outcomes may easily be achieved through processes independent of the program and an evaluation of those outcomes would suggest program success when in fact external outputs were responsible for the outcomes.²⁰

Various types of logic models

The Inputs → Activities → Outputs → Outcomes template

Many authors and guides use the following template when speaking about logic model:^2122232425

Many refinements and variations[which?] have been added to the basic template. For example, many versions of logic models set out a series of outcomes/impacts, explaining in more detail the logic of how an intervention contributes to intended or observed results.²⁶ Others often distinguish short-term, medium-term and long-term results, and between direct and indirect results.

Intervention mapping logic models

Main article: Intervention mapping

The intervention mapping approach of Bartholomew et al.²⁷ makes an extensive use of the logic model through the whole life-cycle of a health promotion program. Since this method can start from as far as a vague desired outcome (author's example is a city whose actors decide to address "health issues" of the city), planners go through various steps in order to develop effective interventions and properly evaluate them. There are distinguishable but closely interwoven logic models with different purposes that can be developed through the process:

• Logic model of the problem, which is a graphical depiction of at-risk population and its social environment behaviors (factors) leading to the health problem and their respective causal pathways (attitudes, beliefs, skills, etc.). This may include as well at-risk population physical environment related causes such as pollutants or lack of physical activity infrastructure and their respective causes, i.e. environmental agents behaviors leading to the physical environment causes and their respective causal pathways;
• Once the most relevant behaviors and causal pathways are identified, planners develop a logic model of change. This is a model of behavioral changes (performance objectives) that should happen and their corresponding necessary changes higher up in the cause-effects chain.
• Finally, a logic model of the intervention is developed. This model describes the various activities that will happen and the cascades of effects they are expected to cause toward the desired outcome.

Evaluators thereafter use the logic model of the intervention to design a proper evaluation plan to assess implementation, impact and efficiency.

Progressive Outcomes Scale Logic Models (POSLM)

The Progressive Outcomes Scale Logic Model (POSLM) approach was developed by Quisha Brown in response to the racial wealth gap [exacerbated by the COVID-19 pandemic] to aid organizations in the immediate need to add a racial equity focus when developing program logic models. More testing and research is needed in order to verify the validity of this model.

The POSLM approach makes use of the logic model with a strong focus on tracking progressive improvement towards racial disparity outcomes. To measure the progress towards outcomes, this type of logic model states short, intermediate and long-term outcomes as "stage 1", "stage 2" and "stage 3". Each stage is uniquely defined and used to depict the percentage of KPIs achieved at each stage or the percentage of people who reach each stage as they progress on pre-identified Key Performance Indicators (KPI). These KPIs are specific to the racial disparity issues which the population served identifies with (i.e. low reading, financial literacy, unemployment, etc). In an effort to prevent the logic model itself from being cluttered with an overwhelming number of KPIs, the KPIs are arranged by category and only the category is displayed on the logic model. The extensive list of KPIs are an appendix to the logic model. Organizations identify the KPIs and corresponding outcomes by first conducting a needs assessment and/or community focus groups. This helps to ensure that the logic model remains focused on improving the real-time needs of people to remove racial barriers. The POSLM can help to make more clear the intended outcomes and the casual pathways leading to them; both of which help to connect and compose a logical companion "if, then" theory of change statement. Again, more research is needed and currently being conducted as more nonprofits, philanthropic and governments use this model.

See also

• Theory of change
• Backcasting
• Critical theory
• Scenario planning
• Thought experiment

Further reading

• Hernandez M, Hodges S (July 2003). "Crafting logic models for systems of care: Ideas into action.". National Evaluation of the Comprehensive Community Mental Health Services and their Families Program.
• Alter C, Murty S (1997). "Logic modeling: A tool for teaching practice evaluation". Journal of Social Work Education. 33 (1): 103–117. doi:10.1080/10437797.1997.10778856.
• Knowlton LW, Phillips CC, Phillips C (2013). The logic model guidebook : better strategies for great results (2nd ed.). Los Angeles: SAGE C. ISBN 978-1-4522-1675-1. OCLC 791492618.
• Conrad KJ, Randolph FL (1999). "Creating and using logic models: Four perspectives". Alcoholism Treatment Quarterly. 17 (1–2): 17–32. doi:10.1300/J020v17n01_02.
• Hernandez M (2000). "Using logic models and program theory to build outcome accountability". Education and Treatment of Children. 23 (1): 24–41.
• Julian DA (1997). "The utilization of the logic model as a system level planning and evaluation device". Evaluation and Program Planning. 20 (3): 251–257. doi:10.1016/S0149-7189(97)00002-5.
• McLaughlin JA, Jordan GB (1999). "Logic models: A tool for telling your program's performance story". Evaluation and Program Planning. 22 (1): 65–72. doi:10.1016/S0149-7189(98)00042-1.
• Stinchcomb JB (2001). "Using logic modeling to focus evaluation efforts: Translating operational theories into practical measures". Journal of Offender Rehabilitation. 33 (2): 47–65. doi:10.1300/J076v33n02_04. S2CID 141946096.
• McCawley PF (2001). "The Logic Model for Program planning and Evaluation" (PDF). University of Idaho Extension. Archived from the original (PDF) on 17 November 2010.
• Savitz S, Matthews M, Weilant S (2017). "Assessing Impact to Inform Decisions: A Toolkit on Measures for Policymakers". RAND.
• "A set of three online evaluation tools that includes a Logic Model Builder". Innovation Network's Point K Logic Model Builder. 2006.
• Unrau YA (2001). "Using client exit interviews to illuminate outcomes in program logic models: A case example". Evaluation and Program Planning. 24 (4m): 353–361. doi:10.1016/S0149-7189(01)00029-5.

References

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