Whereas logic models seem to a more formal method of delineating the nature of a program and evaluation, ripple effect mapping seems be a very social way of engaging not only primary intended users but community members and comprehensive stakeholders as well.

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Work 1)

Whereas logic models seem to a more formal method of delineating the nature of a program and evaluation, ripple effect mapping seems be a very social way of engaging not only primary intended users but community members and comprehensive stakeholders as well. In this process, these populations are considered in terms of the effects of a program and an evaluation has. Ripple effect mapping, I would say, is a very Deweyan approach to facilitating key conversations with stakeholders and users. However, I do not see logic models and ripple effect mapping as being mutually exclusive. I believe they are inseperable. Logic models visually delineate the various inputs, outputs, outcomes, and effects of a program and an evaluation. On the other hand, ripple effect mapping provides a way in which to discuss the components of a logic model, albeit indirectly, and bring the ideas from visualization into realization. It appears to be a very organic process that has authentic and honest implications for U-FE, a sort of simulation if you well. It allows those involved to bring the logic model into real world scenarios and analyze the various effects decisions can have on stakeholders and users.

Work 2)

The difference between Logic Model and Ripple Effect Mapping (REM) is identifying intended and unintended impacts of the program. Logic Model focuses on desired and intended outcomes in the program. These outcomes would be short, medium, and long-term outcomes. However, REM can identify unintended outcomes of the program as well. In this regard, combination of Logic Model and REM would be better for program evolution. Also, program evaluation would be more effective by implementing both Logic Model and REM.


As I started reading this article, I had to back up and take a second glance, re-read the first page and accept, they were really telling me not to stress about the p value. Statistically significant was extremely significant in every single assignment completed for Stats class. 0.05 was a game changer. We were told it had life threatening effects in the medical field in reference to treatment and medication recommendations. This article puts less pressure on the exactness of the statistically significant variable and more emphasis on being authentic. Some key points quotes I felt were important:

No p-value can reveal the plausibility, presence, truth, or importance of an association or effect.

Accept uncertainty and embrace variation in effects.

Be thoughtful in your research.

Be open to transparency and communication

Be modest (so important to me personally)

The nexus of openness and modesty is to report everything while at the same time not concluding anything from a single study with unwarranted certainty.

The analogy on why to eliminate p-value in comparison to the use of money really made it clear that placing a statistical value on every research topic begins to rank or compare our research to all other research. I see this everyday in special education. We associate WISC scores and IQ scores with ability levels and assume with a certain score it is not possible to rise above the stereotype associated with that score. The conclusion shared so many reasons not to use the p-value I couldn’t possibly list all of the lines that I highlighted as I read the article. I think it is very important to explain why you would use a p-value in your research if you chose to include it.

Work 4)

First, I am not a big P-value fan or statistical person. This article explained beyond what we have knowledge. Statistic research has been multiple validity decisions to evaluate, and validate it. So the article demonstrates that we should focus on other results beyond the numbers and the statistical output. In my opinion in some field numbers are not showed the researcher right data. It needs a story to live with.

Work 5)

It is an interesting and complex article because it is different from what we have learned in the statistics class about p-value. We always compare our results with p-value, which is significant value in our work. However, in this article indicate that p-value is not important.

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