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Clearly, both of these factors are manifestations of the force of gravity. 3.1.2 Laminar groundwater ow and the concept of head But is it really reasonable to extend the analogy of a pipe system like that in Figure 3.1b to a real groundwater ow system After all, the subsurface is not composed of stacks of pipes with U-bends! Rather, as we saw in 1, aquifers contain myriad pores which are connected to each other in three dimensions. So why should such assemblages of pores behave as if they were pipes The answer to this question probes deep into the most fundamental differences between the patterns of ow one can observe in a surface stream and those which typify nearly all groundwater ow systems. As was explained in Section 1.4, the limiting factor on the maximum feasible rate of water movement through rocks is the resistance to ow offered by the pore necks, through which water must pass from one pore to another. In all but the most cavernous of rocks, this resistance is such as to effectively limit the rate of movement of groundwater to very low velocities. Flow rates of less than one millimeter per day are commonplace, while a groundwater moving at more than a few centimeters per day would be regarded as speeding! At such low velocities, water tends to move in a very smooth and orderly manner. There is none of the chaotic mixing and splashing which is so characteristic of turbulent ow in stream channels. Rather, groundwater tends to ow as if it were a stack of separate layers, much like the individual cards

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First, one creates m imputed data sets, such that each data set contains a different imputed value for every missing value The value of m can be anything greater than 1, but it typically ranges from 5 to 20 Second, one analyzes the m data sets, saving the parameter estimates and standard errors from each Third, one combines the parameter estimates and standard errors to arrive at a single set of parameter estimates and corresponding standard errors Implementation of Multiple Imputation There are many implementations of multiple imputation An excellent option is Schafer s (1997) set of programs, headed by the NORM program for multiple imputation under the normal model This program works with continuous data, but it has been shown to perform well with categorical data (3+ categories with no missing data, 2 category data with missing data).

Wakeup intervals (s)

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NORM performs well with nonnormal data and with small sample sizes (Graham & Schafer, 1999) NORM can also be used with longitudinal panel data and with cluster data, as long as the number of clusters is relatively small (cluster membership in k clusters is modeled as k 1 dummy codes, which are included in the multiple-imputation model) Schafer s (1997) NORM program (or any normal-based imputation procedure) is an excellent choice with most longitudinal data sets Many longitudinal models for example, standard growth models are fully captured by the NORM model Nothing is gained in this context by using specialized, general linear mixed model programs, such as Schafer s PAN program It is only under special longitudinal circumstances (eg, when all cases are missing for one variable at one point in time, or when some pair of variables is missing for all.

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