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For Academic


Kluger, A. N. (2021, August). Exposing the underlying assumptions of listeners, listening researchers, and listening trainers. The 1st Israeli Listening Research Conference, Ono Academic College, Ramat Gan, Israel.


Kluger, A. N. (2015, April). Listening: Why should you and why should you not? Symposium presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.


SLIDES from the SIOP 2015 conference:

An introduction (Slides 1-9) and "The Hidden Power of Listening: Meta-Analyses"

"If You Listen to Me, I Will Change My Attitude" by Guy Itzchakov


"Listener Effects on Psychological Safety – Attenuated by Avoidance-Attachment Style" by Dotan R. Castro

"Listening Up the Status Hierarchy: Unpacking the Social Status implications of Listening" by Anat Hurwitz.

"Leadership & Listening: A Meta-Analysis"



Bouskila-Yam, O., & Kluger, A., N. (2011). The Facilitating Listening Scale (FLS). Paper presented at the 1st Israel Organizational Behavior Conference, Tel Aviv, Israel. 


Kluger, A. N., & Bouskila-Yam, O. (in press). Facilitating Listening Scale: (Bouskila-Yam & Kluger, 2011, December). In D. L. Worthington & G. D. Bodie (Eds.), The sourcebook of listening research: Methodology and measures. West Sussex, UK: Wiley-Blackwell.

R code

R Codes for Dyadic Data Analysis

Chapter 1 


Table 1.3

Converting data structures: Individual to Dyad, Dyad to Individual, and Individual to Pairwise 

Chapter 2 


Table 2.1

Computing intra-class correlation with confidence interval for Table 2.1

Chapter 3 


Table 3.1

Between dyads variable

Table 3.3 -- example different from book

Within dyads variable


Table 3.5 

Differencing between dyads 

Three ways to calculate ICC


Chapter 4 

Table 4.3 

Multi-level Modeling


Chapter 5


Table 5.1 

 SEM for dyads with indistinguishable members


Chapter 6

                Table 6.1    Equality of variances

                Table 6.2    Equality of correlations

                Table 6.3    Latent variable correlations

                Table 6.4    Omnibus test of distinguishability                                                                    Correlations pairwise method (p. 138)

Chapter 7

Table 7.1    Mimic APIM with MLM results obtained with SPSS  in Table 7.3

                 Figure 7.2  Estimate indistinguishable APIM with SEM

                 Figure 7.3  Estimate distinguishable APIM (with SEM)

Chapter 8

                Table 8.4    Univariate SRM

                Table 8.9    Bivariate SRM

Chapter 9

                Table 9.1    Family SRM

Chapter 10

 p. 292        Blog. One with many reciprocal design -- indistinguishable

p. 292        One with many reciprocal design -- indistinguishable with MLM


p. 293        One with many reciprocal design -- with SEM​ For R-based web applications, see Randi Garcia and Dave Kenny's Dyadic Data Analysis 2018 Workshop

Chapter 11

 Table 11.4    Social network analysis

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