A step-by-step approach to using SAS for univariate & by Norm O'Rourke PDF

By Norm O'Rourke

ISBN-10: 0471469440

ISBN-13: 9780471469445

ISBN-10: 1590474171

ISBN-13: 9781590474174

ISBN-10: 1590477774

ISBN-13: 9781590477779

One in a chain of books co-published with SAS, this ebook offers a ordinary creation to either the SAS method and common statistical methods for researchers and scholars within the Social Sciences. This moment variation, up to date to hide model nine of the SAS software program, publications readers step-by-step in the course of the uncomplicated techniques of study and knowledge research, to info enter, and directly to ANOVA (analysis of variance) and MANOVA (multivariate research of variance).

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Additional resources for A step-by-step approach to using SAS for univariate & multivariate statistics

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In subsequent sections, additional guidelines show how to input the different types of data that are most frequently encountered in social science research. Introduction: Inputting Questionnaire Data versus Other Types of Data This chapter shows how to create SAS datasets in a number of different ways, and it does this by illustrating how to input the types of data that are often obtained through questionnaire research. Questionnaire research generally involves distributing standardized instruments to a sample of participants, and asking them to respond by circling or checking fixed responses.

Here is a directional alternative hypothesis for the preceding experiment: H1: The amount of insurance sold is higher in the population of individuals assigned difficult goals than in the population of individuals assigned easy goals. This hypothesis can be symbolically represented in the following way: H1: M1 > M2 Had you believed that the easy-goal population would sell more insurance, you would have replaced the “greater than” symbol ( > ) with the “less than” symbol ( < ), as follows: H1: M1 < M2 Null and alternative hypotheses are also used with tests of association.

Generally speaking, the experimental group receives the experimental treatment of interest while the control group is an equivalent group of participants who do not receive this treatment. The simplest type of experiment consists of just one experimental group and one control group. For example, the present study could have been redesigned so that it consisted of an experimental group that was assigned the goal of making 25 cold calls (the difficult-goal condition) and a control group in which no goals were assigned (the no-goal condition).

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A step-by-step approach to using SAS for univariate & multivariate statistics by Norm O'Rourke


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