lead to reductions in retest reliability as the total number of transitions (e.g., child positive) get distributed across the different possible antecedent states (e.g., parent negative, neutral or positive). Methodological Considerations The first and most important principle is to plan to get retest data (i.e., a second session) at least on a random subsample of the participants. Even with an hour of observation, high levels of FTS reliability and well trained coders, our retest reliabilities were well below standard recommended levels for use in regression models which leads to loss of power and attenuation of effect size. All dyadic social behavior should be assumed to be highly dependent on the immediate social contextual created on the spot by the dyad and hence have inadequate retest reliability even in highly structured problem solving paradigms until proven otherwise. Do not accept coder agreement as a substitute for retest reliability. Cost effective and statistically efficient designs for the observation of social behavior is an area in which more research is badly needed. Second, compute CSL estimates of log hazards and use small MMSA models to determine their reliability as proxies for latent log hazards. If the reliabilities are high, like .90 or higher, use them instead of creating latent log hazards with MMSA. This will vastly speed up computations and allow fitting more complex and realistic models. If the reliabilities are low, then CSL estimates might still work, especially if multiple sessions of observation are available but the CSL estimates will probably be badly censored (i.e., many zero rates). Models for censored normal variables are available in some SEM programs (e.g., Mplus) and if the censoring point is essentially the same for all subjects, these may be faster computationally than MMSA (see Stoolmiller, Eddy & Reid, 2000 for an example). This is another area that needs more research. MMSA might also benefit from newer, 2-step approaches to regression calibration that have appeared recently. For example, Skrondal and Kuha, (2012) focused on speeding up models for logistic
lead to reductions in retest reliability as the total number of transitions (e.g., child positive) get distributed across the different possible antecedent states (e.g., parent negative, neutral or positive). Methodological Considerations The first and most important principle is to plan to get retest data (i.e., a second session) at least on a random subsample of the participants. Even with an hour of observation, high levels of FTS reliability and well trained coders, our retest reliabilities were well below standard recommended levels for use in regression models which leads to loss of power and attenuation of effect size. All dyadic social behavior should be assumed to be highly dependent on the immediate social contextual created on the spot by the dyad and hence have inadequate retest reliability even in highly structured problem solving paradigms until proven otherwise. Do not accept coder agreement as a substitute for retest reliability. Cost effective and statistically efficient designs for the observation of social behavior is an area in which more research is badly needed. Second, compute CSL estimates of log hazards and use small MMSA models to determine their reliability as proxies for latent log hazards. If the reliabilities are high, like .90 or higher, use them instead of creating latent log hazards with MMSA. This will vastly speed up computations and allow fitting more complex and realistic models. If the reliabilities are low, then CSL estimates might still work, especially if multiple sessions of observation are available but the CSL estimates will probably be badly censored (i.e., many zero rates). Models for censored normal variables are available in some SEM programs (e.g., Mplus) and if the censoring point is essentially the same for all subjects, these may be faster computationally than MMSA (see Stoolmiller, Eddy & Reid, 2000 for an example). This is another area that needs more research. MMSA might also benefit from newer, 2-step approaches to regression calibration that have appeared recently. For example, Skrondal and Kuha, (2012) focused on speeding up models for logistic
Handbook%20of%20Coercion%20v1.docx