gives the death rate. The higher the hazard of death, the more likely a currently living subject will die in the next short period of time. For behavioral observation, the hazard rate usually refers to repeatable, everyday behaviors, not so called trapping states like death. As such, it represents the likelihood of a subject performing the target behavior given that they are currently capable of doing so which at a minimum means they are not currently engaging in the behavior. You are not at risk of performing the behavior if you are currently performing the behavior. If we are recording behaviors because we are interested in models for hazard rates, we have to deal with the situation where the target behavior does not happen during the time we have available to observe. We call this simple right censoring (SRC). Models for hazard rates incorporate information about SRC to avoid biased estimates. SRC always happens unless we can observe subjects continually and forever. Other types of censoring commonly happen in MMSA as well, the most important being so called “competing risks censoring” (CRC). To understand CRC, suppose a parent can be in one of 3 states, negative (A), neutral (N), or positive (P) during an observation session. If the parent is currently neutral, N, then the parent is at risk for a state change to A or P. The state with the higher hazard rate will tend to happen first (say for example, P) and this event will censor the observation of a state change to A. P and A are competing and the winning state P has its winning time recorded along with the fact that it won and the losing state A has the same time recorded but along with the fact that it lost, i.e., was censored by P. CRC can be extensive in observation data and must be incorporated in MMSA to avoid biased estimates of hazard rates. Figure 1 shows a heuristic path diagram for a simple MMSA with 2 hazard rates, the initiation rate of child anger for session 1 and 2. These rates are predicted by concurrent child antisocial behavior (fall K child antisocial) and in turn predict change in future child antisocial behavior in the spring of grade 3. We will use the model in Figure 1 extensively to illustrate various applications of MMSA including models for estimating retest reliability of a single occasion (i.e., session) of observation and models for
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