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interpreting studies leads to false conclusions about what the study results mean; in fact, the traditional approach to data analysis makes it virtually impossible to reach correct conclusions in most research areas (Hunter, 1997; Hunter & Schmidt, 1990a; Schmidt, 1996) Second, meta-analysis leads, by contrast, to the correct conclusions about the real meaning of research literatures These principles are illustrated and explained in more detail in Hunter and Schmidt (1990a); for a shorter treatment, see Schmidt (1996) The reader might reasonably ask what statistical methods researchers should use in analyzing and interpreting the data in their individual studies If reliance on statistical signi cance testing leads to false conclusions, what methods should researchers use The answer is point estimates of effect sizes (correlations and d values) and con dence intervals.

Informally speaking, a database (DB) is simply a collection of data stored on a computer, and the term distributed simply means that more than one computer might cooperate in order to perform some task. Most people working with distributed databases would accept both of the preceding de nitions without any reservations or complaints. Unfortunately, achieving this same level of consensus is not as easy for any of the other concepts involved with distributed databases (DDBs). A DDB is not simply more than one computer cooperating to store a collection of data this de nition would include situations that are not really distributed databases, such as any machine that contains a DB and also mounts a remote le system from another machine. Similarly, this would be a bad de nition because it would not apply to any scenario where we deploy a DDB on a single computer. Even when a DDB is deployed using only one computer, it remains a DDB because it is still possible to deploy it across multiple computers. Often, in order to discuss a particular approach for implementing a DB, we need to use more restrictive and speci c de nitions. This means that the same terms might have con icting de nitions when we consider more than one DB implementation alternative. This can be very confusing when researching DBs in general and especially confusing when focusing on DDBs. Therefore, in this chapter, we

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13413 Sequence Number Field This eld speci es a unique sequence identi er for the frame This identi er is 8 bits in length For the beacon frames, this speci es a BSN to keep up with the contiguous beacons that could be sent For the beacon frames, every coordinator stores the BSN in a variable called macBSN This value is initialized to a randomly generated number It is the job of the coordinator to take macBSN, copy it to the sequence number eld, and then increment it by one number after each beacon frame is transmitted For MAC command frames or ACK frames, this number is a DSN This is stored in a variable called macDSN This is done in order not to increment but to match this value to prove the validity of the frames for data transfer.

The algorithms used to produce these random numbers are not part of this standard For a DEV that does not request an ACK, the sending DEV should increment the macDSN variable by 1 after the transmission is sent If the DEV does expect an ACK, then the DEV increments macDSN by 1, waits aAckWaitDuration amount of time, retransmits the data, and then increment macDSN by 1 again 13414 Destination PAN ID Field This eld is 16 bits in length It speci es the PAN identi er of the receiving DEV This eld uses the value of 0xffff to represent a broadcast ID that will be valid for all DEVs within the PAN This, of course, only happens if the DEVs are on the same channel If the destination addressing mode sub eld is set to 0, then this sub eld is not used 1341.

The many advantages of point estimates and con dence intervals are discussed in Hunter and Schmidt (1990b), Hunter (1997), and Schmidt (1996) A recent APA Task Force report on statistical methods in research also discusses the advantages of con dence intervals over signi cance tests (Wilkinson & the Task Force on Statistical Inference, 1999) Our example here has examined only the effects of sampling error variance and low statistical power There are other statistical and measurement artifacts that cause artifactual variation in effect sizes and correlations across studies for example, differences between studies in amount of measurement error, range restriction, and dichotomization of measures Also, in meta-analysis, mean correlations (and effect sizes) must be corrected for downward bias due to such artifacts as measurement error and dichotomization of measures.

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