can anyone please help me with a summary of ur understanding of the key precepts in this treatise on research and its importance in compensation

CAVEATEMPTORBE AN INFORMED CONSUMER Most managers do not read research. They do not subscribe to research journals; they nd them too full of jargon and esoterica, and they 75 see them as impractical and irrelevant However, a study of 5,000 HR managers compared their beliefs to the research evidence in several areas and identied seven common and important misconceptions held by managers.\" The study authors concluded that being unaware of key research ndings may prove costly to organizations. For example, when it comes to motivating workers, organization efforts may be somewhat misguided if they do not know that \"money is the crucial incentive . . . no other incentive or motivational technique comes even close to money with respect to its instrumental value\"?7 So it pays to read the research. There is no question that some studies are irrelevant and poorly performed. But if you are not a reader of research literature, you become prey for the latest business self-help fad. Belief, even enthusiasm, is a poor substitute for informed judgment. Therefore, we end this chapter with a consumer's guide to research that includes three questions to help make you a critical readerand a better-informed decision maker. 1. Is the Research Useful? How useful are the variables in the study? How well are they measured? For example, many studies purport to measure organization performance. However, performance may be accounting measures such as return on assets or cash ow, nancial measures such as earnings per share, operational measures such as scrap rates or defect indicators, or qualitative measures such as customer satisfaction. It may even be the opinions of compensation managers, as in, \"How effective is your gain-sharing plan?\" (Answer choices are \"highly effective," \"effective,\" \"somewhat,\" \"disappointing," "not very effective." \"Disastrous\" is not usually one of the choices.) The informed consumer must ask, Does this research measure anything useful? 2. Does the Study Separate Correlation from Causation? Once we are condent that the variables are useful and accurately measured, we must be sure that they are actually related. Most often this is addressed through the use of statistical analysis. The correlation coeicient is a common measure of association and indicates how changes in one variable are related to changes in another. Many research studies use a statistical analysis known as regression analysis. One output from a regression analysis is the R2. The R2 is a squared correlation and tells us what percentage of the variation in the outcome variable is accounted for by the variables we are using to predict or explain. But even if there is a relationship, correlation does not ensure causation. For example, just because a manufacturing plant initiates a new incentive plan and the facility's performance improves, we cannot conclude that the incentive plan caused the improved performance. Perhaps new technology, wgineering, improved marketing, or the general expansion of the local economy underlies the results. The two changes are associated or related, but causation is a tough link to make. m Too often, case studies, benchmarking studies of best practices, or consultant surveys are presented as studies that reveal cause and effect. They do not. Case studies are descriptive accounts whose value and limitations must be recognized. Just because the best-performing companies are using a practice does not mean the practice is causing the performance. IBM provides an example of the diiculty of deciding whether a change is a cause or an effect. Years ago, IBM pursued a nolayoff policy. While IBM was doing well, the nolayoff deciding whether a change is a cause or an effect. Years ago, IBM pursued a nolayo policy. While IBM was doing well, the nolayoff policy was cited as part of the reason. Later, when performance declined, IBM eventually ended the no-layoff policy as a partial response. Did the policy contribute to company success at one time, but not later due to changing circumstances? Did it always act as a drag on company success? Or was it a mistake to get rid of it? Causality is difcult to infer as we do not know what would have happened had IBM never had the policy and/or if they had it and kept it (versus ending it). Perhaps because of such challenges in inference, compensation research often does attempt to answer questions of causality. Yet good policy decisions rest on making good causal inferences-'78 Thus, we need to strive to overcome the challenges to answer key questions such as: How does the use of performance-based pay inuence employee ability and motivation, customer satisfaction, product quality, and company performance? 3. Are There Alternative Explanations? Consider a hypothetical study that attempts to assess the impact of a performance-based pay program. The researchers measure performance by assessing quality, productivity. customer satisfaction, employee satisfaction, and the facility's performance. The nal step is to see whether future periods' performance improves compared to this period's. If it does, can we safely assume that it was the incentive pay that caused performance? Or is it equally likely that the improved performance has alternative explanations, such as the uctuation in the value of currency or perhaps a change in leadership in the facility? In this case, causality evidence seems weak. Alternative explanations exist. If the researchers had measured the performance indicators several years prior to and after installing the plan. then the evidence of causality is only a bit stronger. Further, if the researchers repeated this process in other facilities and the results were similar, then the preponderance of evidence is stronger yet. It could then be concluded that clearly the organization is doing something right, and incentive pay is part of it. The best way to establish causation is to account for competing explanations, either statistically or through control groups. The point is that alternative explanations often exist. And if they do, they need to be accounted for to establish causality. It is very difcult to disentangle the effects of pay plans to clearly establish causality. However, it is possible to look at the overall pattern of evidence to make judgments about the effects of pay. So we encourage you to become a critical reader of all management literature, including this book. As Hogwarts' famous Professor Alastor Moody cautions, have \"constant vigilance for sloppy analysis masquerading as research?\