CORRELATIONAL RESEARCH: measuring statistical relationships between 2+ variables * correlation studies measure variables; NOT manipulate: 1. Measure one variable (X) 2. Measure second variably (Y) 3. Statistically determine if X & Y are related (X Y) * BIDIRECTIONALITY PROBLEM: X Y‚ X Y‚ or X Y * THIRD VARIABLE PROBLEM: X Z Y * determines if association between variables exists * cannot draw causal conclusions * CORRELATION
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Securing Yourself Online Final Version 1 Attention Catcher: You are at risk. All of us are at risk. I’m not talking about STDs‚ global warming‚ or terrorism. I’m talking about your life online. You’re at risk of having your computer taken over so that perfect strangers can read your e-mail‚ access your bank accounts‚ pilfer your credit card numbers‚ and even steal your identity. 2 Listeners Link: Speakers Credibility: As college students‚ we’re particularly vulnerable because we spend
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Types of Variables Binary variable Obsevations (i.e.‚ dependent variables) that occur in one of two possible states‚ often labelled zero and one. E.g.‚ “improved/not improved” and “completed task/failed to complete task.” Categorical Variable Usually an independent or predictor variable that contains values indicating membership in one of several possible categories. E.g.‚ gender (male or female)‚ marital status (married‚ single‚ divorced‚ widowed). The categories are often assigned numerical
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Group Assignment (Groups of five) a) Come up with a research topic b) From the topic indicate the following 1) The dependent variable 2) The independent variable 3) The extraneous variable 4) The Moderating variable 5) The Intervening Variable Research Topic Impact of a tattoo on a helping request‚ Strohmetz‚ D. B.‚ & Moore‚ M. P. (2003‚ March) A psychologist wondered how people would respond to people with tattoos‚ so he studied
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Radioisotopic labels would be used in experiments to identify semi-conservative replication in prokaryotes. Because we anticipated that a labeled DNA would have different density with unlabeled‚ which means‚ by analyzing the different density of DNAs‚ we can determine which of DNA is labeled‚ half-labeled or unlabeled. To this end‚ I will use c13 label the bacteria and abruptly change carbon source with C12. Then I will collect four samples in different time and analyze the results from centrifugal
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Variables 1 Corporate variables ADD1 -- Address Line 1 ADD2 -- Address Line 2 ADD3 -- Address Line 3 ADD4 -- Address Line 4 ADDZIP -- Postal Code BUSDESC -- S&P Business Description CITY -- City CONML -- Company Legal Name COUNTY -- County Code DLRSN -- Research Co Reason for Deletion EIN -- Employer Identification Number FAX -- Fax Number FYRC -- Current Fiscal Year End Month GGROUP -- GIC Groups GIND -- GIC Industries GSECTOR -- GIC Sectors GSUBIND -- GIC
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Are we too dependent on computers? When we talk about computers‚ people often relate computers with modern technologies. Computer is a tool that helps us make things in our life easier. People use computers in business‚ public services‚ educations‚ even entertainment. Our daily activities are more and more based closely on the working of computer. Almost everything we do is affected by modern technology and computers. I would say that I agree that people nowadays depend on computers too
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Controlled Assessment ISA (PU 2.5) Light Dependent Resistors (Section 1) 1. Write down your hypothesis. Explain why you made this hypothesis. Hypothesis: When it is day there is more light hitting the LDR and so its resistance decreases and the light turns off. When it is night there is less light so the LDR has higher resistance and the light turns on. Explanation: When light shines on a Light Dependent Resistor‚ electrons are released and the current can get larger. So as the light intensity increases
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descrip DESCRIPTIVES VARIABLES=StudentPreparationSP GPA FBT /STATISTICS=MEAN STDDEV MIN MAX KURTOSIS SKEWNESS. Descriptives Notes | Output Created | 14-NOV-2012 14:18:38 | Comments | | Input | Active Dataset | DataSet1 | | Filter | <none> | | Weight | <none> | | Split File | <none> | | N of Rows in Working Data File | 764 | Missing Value Handling | Definition of Missing | User defined missing values are treated as missing. | | Cases Used | All
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Use of Dummy Variables in Testing for Equality Between Sets of Coefficients in Linear Regressions: A Generalization Author(s): Damodar Gujarati Source: The American Statistician‚ Vol. 24‚ No. 5 (Dec.‚ 1970)‚ pp. 18-22 Published by: American Statistical Association Stable URL: http://www.jstor.org/stable/2682446 . Accessed: 09/07/2013 18:34 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use‚ available at . http://www.jstor.org/page/info/about/policies/terms
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