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Revisando las implicaciones de la pandemia para l@s evaluador@s

Reviso el post de hace ahora casi dos años de MQPatton del 23 de Marzo de 2020, Implicaciones en la evaluación de la actual pandemia,  MQPAtton nos da su visión sobre la pandemia y las implicaciones que puede tener para la evaluación. Esta era su opinión sobre dónde estabamos y hacia dónde deberíamos ir como evaluador@s:

  1. Adaptemos ahora los planes y diseños de evaluación.
  2. Seamos proactivos: esto no va a pasar rápidamente.
  3. Centrémonos en el uso, no en nosotr@s.
  4. Reglas de datos en tiempo real.
  5. Consideremos el estándar de rigor «suficientemente bueno». Separarse del rigor como un estándar metodológico absoluto.
  6. Todo cambia en una crisis. Abraza el cambio, no te resistas.
  7. Participa en el pensamiento sistémico.
  8. Piensa globalmente, actúa localmente.
  9. Prepárate para defender el valor de la evaluación.
  10. Tengamos conocimiento, seamos un verificadores de hechos.
  11. Modela el pensamiento evaluativo sistemático.
  12. Aboga por mejores datos.
  13. Destaquemos la necesidad de una transformación hacia la sostenibilidad global a más largo plazo.
  14. Sigue aprendiendo.
  15. Apoyarse mutuamente como comunidad de evaluación.

Sería interesante revisar, ahora o en un tiempo, estos 15 puntos y reflexionar en torno a cómo respondió la evaluacion durante la pandemia, especialmente los puntos 7, 9, 11 y 15.

Escucha y activa

Fuente

Fuente

Escuchar, argumenta Erich Fromm, “es un arte, como la comprensión de la poesía” y ofrece algunas pautas para dominar el arte de la comprensión desinteresada: (1) la concentración completa del oyente, (2) librarse de ansiedad, (3) imaginación que funcione libremente y que pueda expresarse en palabras y (4) la capacidad de empatía con otra persona para tratar de comprender a otr@.

La escucha activa es una técnica, una forma / estrategia específica comunicación y/o un “arte” que: 

(1) requiere “disponibilidad, interés por la persona, comprensión del mensaje, espíritu crítico y prudencia en los consejos”,

(2) consiste en una forma de comunicación que ofrece disponibilidad y muestra interés por la persona que habla y demuestra que el oyente le ha entendido.

(3) no se limita a oír y dejar hablar al interlocutor sin interrumpir su discurso, sino que necesita de una atención física, psicológica y verbal, estar totalmente concentrados en el mensaje que el otro individuo intenta comunicar.

(4) se refiere a la habilidad de escuchar no sólo (a) lo que la persona está expresando directamente, sino también los (b) sentimientos, ideas o pensamientos que subyacen a lo que se está diciendo.

 

Algunas barreras para la escucha activa son (1) hablar excesivamente, (2) prejuzgar, (3) distracciones, (4) esperar o dar por supuesto que otros comparten nuestras creencias y valores personales, (5) malentendidos, (6) interrumpir o interrupciones, (7) hacer que escuchamos pero falsificando la atención, (8) dejarse llevar por las emociones, (9) el ruido y/o (10) el miedo o el temor.

 

En fin, esto de la escucha activa es también un filón de oro para el campo de la evaluación, y ¿no recuerda un poco a la canción de Carla Morrison? «…es escalofriante, tenerte de frente, hacerte sonreír, daría cualquier cosa por estar siempre aquí y entre todas esas cosas, no te fallaré, quiero perder contigo mi tiempo, guardar tus secretos, cuidar tus momentos, esperarte, adorarte, tenerte paciencia, tu locura es mi ciencia…»

The Role of Support in Innovation

The Beatles’ song made legendary by Joe Cocker speaks of getting by with a little help from our friends. The role of friends — associates, collaborators, trusted allies, partners — is vital to making innovation happen.

The myths about change-makers and innovators are many: The self-made woman/man, the great innovator, the great mind who works long and hard to succeed because of their own cleverness or ingenuity, the entrepreneur who transforms a market all by herself, a leader who takes an organization to new heights. Take your pick.

However, the evidence is clear: you need supporters to succeed. Whether it is early-stage support for ideas and potential or to deliver the finished product, support is critical to innovation. This was the topic that we recently covered on the latest episode of Censemaking: The Innovation Podcast.

Look at the list below and you’ll see that most core items involve some kind of support mechanism either through teams, senior leadership, or markets.

Testing for innovation
https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/the-eight-essentials-of-innovation

Three Lessons for Support Generation

There are three core lessons from the literature on what to do:

  1. Find a tribe, build a community, join in with others. Whatever it takes, connect with those in your market, outside your market and those you wish to serve. Engage.
  2. Be a friend to have a friend. Share ideas, lessons learned, and assist your community however you define that. We see time and again that the best organizations are known and respected because they give from themselves. That also ensures that they receive just as much. You’re much more likely to attract the kind of knowledge that you need, the skills that you need when you share the knowledge and skills that you have. Creating a connected community of people who ‘get’ you is important.
  3. Leave the heroes to the comic books. Heroes make for great stories, but really lousy, real life models for change. What you need is a supportive structure. Mastermind group leadership teams, peer meetups – they all make a difference in reducing isolation and increasing the amount of contact points you have so that you can generate ideas and do so in a group that understands you. These can be internal or external — but they must allow for support to be gained and received.

You can’t do it alone. Find ways to connect with others who are doing something similar to what you’re doing, that support, which will be different for everybody, but that difference makes all the difference.

If you want to build a strategy to create connections within your organization contact us — we can help. We can also help you build the kind of internal structures to learn, share ideas, and innovate.

Photo by Neil Thomas on Unsplash

The post The Role of Support in Innovation appeared first on Cense Ltd. .

Finding the Right Sample Size (the Hard Way)

In our previous article, ‘Finding the Right Sample Size (the Easy Way)’, we discuss the importance of determining the so-called “correct sample size”. Our recommendation for most applications was to use an online sample size calculator (check out our calculator HERE).

However, for those interested in calculating sample sizes by hand, or getting a better understanding of the math behind many of these sample size calculators, we outline the formulae used to calculate sample sizes. 

Estimating sample sizes (The Hard Way) 

Sample sizes can be estimated using statistical formulae by hand. While not recommended, it is important to have a basic understanding of how sample sizes are being estimated when using a tool. 

First, some definitions. 

  • Margin of error: The margin of error is how much you can expect your results to differ from the population of interest. Measured as a percentage, a smaller margin of error increases the chance that your results will be close to that of the population. Both 5% and 10% are commonly used margins of error. However, lower margin of errors will increase your sample sizes.  

  • Confidence level: The confidence level is a percentage the represents how confident you can be that the true percentage of a population (i.e., a measured value, such as participant responses to a survey question) falls within the margin of error. This value is usually 95%, but 90% and 99% are also common. Larger confidence levels will increase your sample sizes. 

  • z-score: A z-score is a value that determines how far a measured value is from the population value. z-scores can be determined from the confidence level using z-score tables (see Z Score Table for more information). 

  • Population proportion: The population proportion is the percentage of the population that has a specific characteristic. This proportion is usually determined from previous studies or research. Although, when unsure, using 50% works as an estimate. That is, 50% of the population falls below a specific point and 50% falls above a specific point. 

Calculating an estimated sample size 

The following outlines the specifics of Cochran’s sample size formula. Using the unlimited formula based on your own estimates of the z-score (based on your confidence level), population proportion, and margin of error, you can get an estimate of a sample size required for a population of unlimited size. However, this is not realistic as populations are finite. Therefore, you can take the sample size estimate from the unlimited population formula and insert it into the finite population formula. This considers the size of the population of interest and provides a better estimate of the sample size based on your needs.  

Unlimited population: 

where: 

  • n is the sample size 

  • z is the z-score 

  • p̂ is the population proportion 

  • ε is the margin of error (confidence interval) 

Example for unlimited population: 

where: 

  • z = 1.96 (Based on a 5% margin of error. Data are assumed two-tailed (i.e., a margin of error of 2.5% on each end of a normal distribution curve), thus a value of 0.9750 will be looked up within the z-score table.) 

  • p̂ = 50% or 0.50 (This value is often pulled from previous research/ literature. If unsure, use 50%.) 

  • ε = 5% or 0.05 (Same value used to get the z-score estimate but provided as a decimal/ percentage.) 

Finite population: 

where: 

  • n is the sample size 

  • z is the z-score 

  • p̂ is the population proportion 

  • ε is the margin of error 

  • N is the population size 

Example for a finite population: 

where: 

  • n = 385 (Value calculated using the infinite population formula.) 

  • z = 1.96 (Based on a 5% margin of error. Data are assumed two-tailed (i.e., a margin of error of 2.5% on each end of a normal distribution curve), thus a value of 0.9750 will be looked up within the z-score table.) 

  • p̂ = 50% or 0.50 (This value is often pulled from previous research/ literature. If unsure, use 50%.) 

  • ε = 5% or 0.05 (Same value used to get the z-score estimate but provided as a decimal/ percentage.) 

  • N = 1000 (This value is inserted if known and is often pulled from research/ literature or some prior background knowledge about the population of interest.) 

With the above formulae and examples, you will be able to calculate sample sizes on your own.

We would still suggest using an online calculator to do the heavy lifting, but having a better understanding of the math behind sample size calculation never hurts!


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