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2/3 & 2/5 lecture notes, week 3

by: Alyssa Reyes

2/3 & 2/5 lecture notes, week 3 PSYC 2301

Marketplace > University of Houston > PSYC 2301 > 2 3 2 5 lecture notes week 3
Alyssa Reyes
Intro to Methods in Psychology

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About this Document

Lectures 3, 4, and 5 notes are included. I use the professor's slides and put in my own notes and "doodles" to help visualize some of the concepts. Enjoy! :)
Intro to Methods in Psychology
Class Notes
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This 4 page Class Notes was uploaded by Alyssa Reyes on Thursday February 5, 2015. The Class Notes belongs to PSYC 2301 at University of Houston taught by Breitmeyer in Spring2015. Since its upload, it has received 118 views.

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Date Created: 02/05/15
1 RELATION OF VARIABLES TO RESEARCH METHODS 1 Experimental Method i Can be used to draw causal Field experiment doing inferences How does systematically research quotoutside of a lab in a manipulating one variable produce nat ral settin Done b 39 I systematic changes in another u g39 y pp Interested In personality variable social psychology ii two aspects of method I manipulation of one variable EX Ef e Of 3th 0 pp39 5 behaVIor Does It cause Ueveloftouch 7 observationmeasurement of pos39t39ve re39nforc39ng Value39 another variable dependent Control group person was not variable iii minimal structure of a valid gt ztedrgnh rghgewlcvitge card39 experiment 9 p39 y touched D the variable is touch Result the minimal amount of touch affected the person s Control conditiongroup no touch group Experimenta conditiongroup touched group iv potential issue of concern when using experimental method possibility of confounds Subjectvariabe confounds D let s say 1720 that were touched were female Gender can be a confound variable Treatment confound changes in level of independent variable The person behind the counter smiles smiling can make you feel good also CONCLUSION CAN BE FLAWED 2 Correlational method i Can only be used to describe or predict cannot be used to draw causaI inferences ii uses exporatory studies sleep affecting cognitive performance Studies in which experimental method cannot be used person w brain damage Estabishing relations between measured predictor and criterion variables SATACT scores is a predictor to universities how well you will do in your rst year of coHege Descriptive research eg surveys behavioral observations case studies archival studies psychological assessments iii Potential issue of concern when using correlational method possibility of intervening third variable Lecture 4 Assessing Quantitative Relationships between Variables 1 BASIC TYPES OF QUANTITATIVE RELATIONSHIPS a Positive Linear increase in one variable is associated with increase in the other variable as SAT scores increases GPA values also increases going up b Negative Linear increase in one variable is associated with decrease in the other variable as speed of response increases accuracy of response decreases going down c Curvilinear increase in one variable is associated with both increases and decreases in the other variable as stimulus complexity increases viewing time increases then decreases parabola OR studying you have a peak point of hours of studying but when you surpass it you can no longer hold more info d No relationship increase in one variable is not associated with any changes in the other variable as adult height increases adult IQ score stays the same straight line e Combination two variables might be related in a more complex l 2 ASSESSING THE STRENGTH AND DIRECTION OF RELATIONSHIPS THE ROLI CORRELATION COEFFICIENTS a Pearson39s r 10 s r s 10 D you cannot have a coef cient of rrlge I than 10 i The sign of r or tells you the direction of the relationship ii The higher the absolute value of r the stronger the relationship The strength of the relationship determines how certain one can predict one variable from another Insert numbers for relationships here iii The strength of a relationship also depends on the range of values taken on by the two variables Restriction of range results in lower r value weaker relationship 3 REGRESSION EQUATIONS b The previous are all examples of simple linear regression equations of the type Y a1X1 b which can be used to predict the value of one variable from the known value of the other For example Y is predicted Exam2 score X1 is obtained Exam1 score c one can also use multiple linear regression equations of the type Y a1X1a2X2aand For example Y is the predicted college GPA X1 is the known SAT score X2 is the known high school GPA and so on d Relative to simple regression equations multiple regression equations increase the accuracy of the predicted outcome the more predictor variables the more accurate the predicted outcome Y relative to the A actually obtained one Y Y is predicted Exam2 score X1 is obtained Examl score Y is the predicted college GPA X1 is the known SAT score X2 is the known high school GPA and so on Lecture 5 Assessing the Reliability the how the Accuracy and the Validity the what of Measures 1 RELIABILITY OF MEASURES Measured Value E quotm Valuequot Random Error The closer the Random Error is to 0 the more reliable the measure a Reliability depends on stability and consistency of i phenomenonobject being measured ii measurement deviceapparatus iii measurement procedure b Rule Samesimilar apparatus and procedures applied to samesimilar phenomena or object will yield samesimilar measured values c Examples of i physical variable such as length a Object your notebook b Measuring device a ruler c Measuring procedure line up one end of notebook with one end of the ruler and record value found at other end of the notebook ii psychological variable such as shyness a Object a particular person b Measuring device a trained observer c Measuring procedure observe the number of eye contacts measure the total duration of eye contacts etc d End result highly replicable measured values ie small random measurement error 2 TYPES OF RELIABILITY OF MEASURES a Testretest reliability measures are stable over time b Alternateform reliability assesses extent to which different versions of a measure yield results that are consistent with each other c Internal consistency assesses whether or not one part of a measure yields results consistent with another part i splithalf reliability ii oddeven reliability iii itemtotal reliability item analysis d interrater reliability 3 ACCURACY OF MEASURES E book doesn39t talk about this much Formula Version 2 Measured Value E quotTrue Valuequot Constant Error Random Error the closer the Constant Error is to O the more accurate the measure 4 THEREFORE IT IS POSSIBLE THAT a Measures can be accurate without being reliable There can be a large random error and small constant error Example You are driving at 60 mhr your speedometer uctuates randomly between 45 and 75 mhr Yet the average of all the observed uctuating readings indicate the true speed of 60 mhr b Measures can be reliable without being accurate There can be a large constant error and a small random error Example You are driving at a constant speed of 58 mhr but your speedometer indicates a non uctuating speed of 67 mhr The constant error is 9 miles per hour the random error is 0 or close to it c What characterizes measures that are accurate and reliable Inaccurate and unreliable Think of throwing at a target wig around the same area reliable but not accurate gt I Not reliable 39 but accurate Not reliable not accurate Reliable and accurate


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