No, that would not be a valid conclusion because the researchers did not follow individuals as they aged from 20 to 50 to 80 years old. Example of cross-sectional research design Would that be a valid (accurate) interpretation of the results?įigure 1. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time they are taking a “cross-section” of people at one point in time. The researchers might choose to give a certain intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. They might have a hypothesis (an educated guess, based on theory or observations) that intelligence declines as people get older. Let’s suppose that researchers are interested in the relationship between intelligence and aging. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time. The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. These techniques try to examine how age, cohort, gender, and social class impact development. Developmental research designs are techniques used particularly in lifespan development research. When we are trying to describe development and change, the research designs become especially important because we are interested in what changes and what stays the same with age. Research design dictates which methods are used and how. Research design is the strategy or blueprint for deciding how to collect and analyze information. But it is easy to confuse research methods and research design. Remember, research methods are tools that are used to collect information. Now you know about some tools used to conduct research about human development. Compare advantages and disadvantages of developmental research designs (cross-sectional, longitudinal, and sequential).For the MNIST database, SMO is as fast as PCG chunking while for the UCI Adult database and linear SVMs, SMO can be more than 1000 times faster than the PCG chunking algorithm. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. Because large matrix xomputation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while a standard projected conjugate gradient (PCG) chunking algorithm scales somehwere between linear and cubic in the training set size. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. SMO breaks this QP problem into a series of smallest possible QP problems. Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO.
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