I never thought about the importance of just listening to each other. Finally, I offer you my favorite offshoot of the dyad: the I-yad! Coined by my brilliant colleague Mark Salinas of the National Equity Project, the I-yad is a tool to draw upon when you need to release emotion without someone trying to solve your problem. Simply walk up to a colleague you trust and say, "Can I have an I-yad please? Try this tool, and watch as a bunch of stressed-out educators lower their shoulders and breathe more deeply while they reconnect with their better professional selves.
When and where do you do your best listening with colleagues? How and in which settings can you imagine using dyads as a listening tool? What other "gut prompts" come to mind?
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Get the best of Edutopia in your inbox each week. By contrast, dyads offer us a form of Constructivist Listening , adapted by the National Equity Project from the National Coalition for Equity in Education , which rests on a few assumptions: Emotions are a natural part of life and deserve a seat at our professional tables.
Emotional distress limits intelligent thinking and caring behavior. When given time to reflect, people have the ability to solve their own problems. As social beings, we construct meaning through language by talking and being listened to. Speakers Need Listeners Here are a few essential steps to make dyads a powerful listening tool. Frame the purpose and guidelines clearly. The level of consequential nonindependence is about.
If there is, then the effective alpha is. For dyads, to have sufficient power to test for consequential nonindependence, there must be at least 35 dyads. If there are less, then the power of the test of nonindependence may be too low.
Thus, in studies with fewer than 35 dyads, a reasonable course of action is to presume that scores are nonindependent because there is not sufficient power to test for nonindependence. First, given that that there are at least about 35 dyads, test whether there is nonindependence. If there are less than 35 dyads, one should treat the data as if they were nonindependent.
Ideally the test for nonindependence should control for the effects of any predictor variables. If test indicates that there is independence, then person can be used as the unit of analysis. If test indicates that there is nonindependence, then dyad should be used as the unit of analysis. The outcome measure is either the sum or average of the two members whichever is more interpretable. It is assumed that preliminary tests have shown that there is nonindependence in the outcome. First, assume that the predictor variable is dichotomous, e.
The paired t-test can be used. The two groups are the two levels of the within-dyads variable. This test in essence tests whether the mean of the difference scores equals zero where the difference is between the scores of the two dyad members. So if we wanted to test whether husbands are more or less satisfied than wives, we would compute a difference score, say wife score minus husband score, and test whether the mean of the differences is significantly different from zero.
Second, assume that the predictor variable is interval, e. Compute difference scores in both X and Y. Regress differenced Y on differenced X without any intercept. The effect of differenced X on differenced Y in this equation measures the effect of X on Y. The intercept is not fitted because the direction of differencing is arbitrary.
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By not including an intercept, the solution will be the same regardless of how the differencing was done. Try it out if you do not believe me! It is instructive to reproduce the paired t test results by fitting a regression equation with no intercept. First, compute a paired t test and note the t and its p value. Now compute difference scores and create a predictor "variable" all of whose scores are that equal two. Run the regression equation, not including an intercept. Note you get the same t and p value.
Now reverse the sign of the first dyad, both of the difference and the predictor variable. Note again that you get the same coefficient the mean difference and the same t and p value. You would not if the intercept were included.
How is nonindependence controlled if there are several predictor variables, some of which are between and others of which are within dyad? One needs to perform two analyses. In the first, the sum or average of the two dyad members' scores is the outcome and the predictors in this equation is the set of between-dyads predictor variables.
Intervals and dyads
The second analysis is of the difference scores and includes the difference scores of the within-dyads variables as predictors and no intercept. Sometimes this second analysis can be accomplished by a repeated measures analysis of variance. There must be one within-dyads variable that is dichotomous, e. It would be treated as the "repeated measure," and dyad, not person, would be the unit of analysis. If there are two dichotomous within-dyads factors, then the following must be done.
One of the within-dyads variables becomes the repeated measure. The other is captured by creating the following between-dyads factor. It codes for the level of second within-dyads factor at first level of the repeated measure. The interaction of this factor with the repeated measures captures the second within-dyads factor.
Interactions of between and within factors are captured by including the between factors in the difference score regressions. Much more detail on this topic is contained in Chapter 3 of Kenny, Kashy, and Cook You can download a bibliography of APIM papers if you click here. Assume that there is a mixed predictor variable, denoted as X, and an outcome denoted as Y.
Denote X i and X i ' as the two scores of the predictor variable for dyad i and Y i and Y i ' as the two scores on the outcome for that same dyad. Basically the model is that X and X' cause Y where the effect of X on Y is called an actor effect and the effect of X' on Y is called a partner effect.
So if a researcher examined the effects of gender on intimacy and studied friendships, both same and opposite gender, there are at least two effects of gender on intimacy. Females could be more intimate than males, an actor effect. Alternatively, interactions with females could be more intimate, a partner effect, For more detail on the analysis of APIM consult Chapter 7 of Kenny, Kashy, and Cook Actor-Partner Interaction.
Many dyadic processes can be viewed as actor-partner interactions. For instance, the effects of similarity between dyad members can be viewed as an actor-partner interaction. The usual way to measure the actor-partner interaction is to multiply actor times partner effects or X times X'. However, if the interaction is supposed to be similarity, then the absolute difference between X and X' would be measured.
Note that the actor-partner interaction is always a between-dyads variable. For any actor-partner interaction that is estimated, one should control for the main effects of actor and partner. Thus, if similarity is to be tested, the main effects of actor and partner should be controlled.
DYAD | definition in the Cambridge English Dictionary
Estimation Practically, there are three ways to estimate this model when there is nonindependence: pooled regressions, structural equation modeling, and multilevel modeling. The pooled regression method has been essentially replaced by the two other two methods, but it is still useful to describe.
Two regression equations are estimated and their results are pooled. In the first, the criterion variable is the average the two Y scores. The predictor variables in this equation include the average of all mixed variables and all the between-dyads variables.
In the second regression equation, the differences between the Xs and Ys are computed. One computes the difference in the same way for all of the variables. In this equation the difference between X and all within-dyads variables differenced are included. However, the intercept is not estimated in this equation. The standard errors need to be pooled and specialized degrees of freedom must be estimated that are described below. In the between-dyads regression, the average of the two members' scores is used and the regression coefficient is denoted as b B.
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