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participants are included and pretreatment levels on key outcomes are substituted for missing data (Flick, 1988; Kendall et al, 1999) However, dropout status may not be equivalent to full relapse, and in applications in which missing data may be common and unavoidable (eg, large-scale programs) the traditional, conservative approach may produce highly misleading results Hence, in recent years, this standard approach has been challenged and a variety of potentially more informative alternatives proposed (Little & Yau, 1998; Shadish, Hu, Glaser, Kownacki, & Wong, 1998) These techniques have in common a goal of estimating the effect on outcome results of a range of assumptions about the status of dropouts However, none of these techniques are substitutes for valid information on participants Therefore, in designing and conducting health behavior change research, minimizing dropouts and missing data is a paramount concern.

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In reporting and evaluating such research, information about the degree of missing data, its distribution across treatment arms, its correlates with pretreatment factors (eg, demographic characteristics, initial severity, etc), and the potential impact on the validity of statistical tests and related conclusions about the signi cance and magnitude of intervention effects (or the lack thereof) must be reported and considered carefully When interventions are delivered to more than one individual at a time, the nesting of observations within groups and the resulting potential dependencies among the observations must be considered Previously unacquainted individuals within small groups, members of a couple or family, students within a given classroom or school, or residents within a community assigned to a treatment and receiving that intervention together do not provide truly independent observations.

Hence, treatment of their data as representing independent observations can seriously violate the underlying assumptions of many traditional statistical techniques Typically this failure to recognize dependencies leads to an overestimate of the signi cance of intervention effects (Feng, Diehr, Peterson, & McLerran, 2001) The most common version of this problem is when psychosocial interventions are delivered in small groups of previously unacquainted participants Unique features of individual groups, such as positive versus negative group climate, cohesion, or morale, can alter the effectiveness of treatment (Etringer, Gregory, & Lando, 1984) This issue is problematic in many areas of health behavior change (eg, Rooney & Murray, 1996) and is quite familiar in other areas of traditional psychological intervention research (Crits-Christoph & Mintz, 1991) Recent HLM models and other techniques can be adapted to this design problem, with additional bene ts (eg.

The front of ce is often seen as an information source and a request center for guests and hotel employees. Front of ce staff may need to eld questions such as: Has the front

, more ef cient management of missing observations) beyond the accurate.

It may not always be possible to transmit sensor data frequently, due to hardware failures, environmental conditions, and the presence of physical layer jamming Furthermore, frequent transmissions over the radio are an expensive operation in terms of energy consumption Due to these and other factors, a sensor node must be able to store data locally over a long period of time while multiple sensor readings are collected At present, most sensor nodes only have around 4 12 kb of RAM, enough to accommodate data processing, but insuf cient for long-term logging of data However, sensor nodes also provide a relatively large amount of persistent storage through external ash memory To take advantage of this persistent storage, an abstraction layer must be provided that enables applications to access this memory in a manner that is simple, ef cient, and reliable.

While a simple circular buffer may suf ce for the most rudimentary programs, this approach cannot satisfy the needs of more complex applications, such as the Mate virtual machine [18] In order to support complex applications, as well as to provide a reliable logging mechanism for sensor data, a sensor network operating system must include a le system A le system tailored for sensor nodes needs to take into consideration a multitude of resource constraints, and it should be optimized for the common operations and data types observed in a sensor network The three most commonly encountered data sources in a sensor network are con guration data (sampling rate, various thresholds, etc), sequentially appended sensor data, and binary code images Clearly, the most common operation on les would be sequential appending and reading of sensor data.

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