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by: Kaya Conroy


Kaya Conroy
GPA 3.85


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Class Notes
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This 16 page Class Notes was uploaded by Kaya Conroy on Thursday October 22, 2015. The Class Notes belongs to RG ST 201 at University of California Santa Barbara taught by Staff in Fall. Since its upload, it has received 50 views. For similar materials see /class/227181/rg-st-201-university-of-california-santa-barbara in Religious Studies at University of California Santa Barbara.

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Date Created: 10/22/15
Ed201 Survey Research Design Winter 2007 Week 5 Lecture Notes Questionnaire Design What we ll cover today 1 Review homework assignment 2 2 Overview of measurement issues 3 Questionnaire design Homework 2 only one student got all the answers right review questions and answers Measurement A Overview of Measurement Process CONCEPT VARIABLE MEASURE l Conceptualization process of formulating and clarifying concepts eg socioeconomic status achievement prejudice selfesteem 2 Speci cation defming speci c aspect or indicator of concept some concepts may require only a single indicator e g age gender others may require several indicators to measure concept because a errors of classificationmeasurement b single indicator may not capture all the aspects of concept eg socioeconomic status called composite measures or composite variables eg SES 3 Operationalization process of developing procedures for measuring variables sources of information observation interviews questionnaires tests etc maybe more than one method e g selfreport observation archival eg absenteeism HOW procedures depend on variable and concept some variables are simply descriptive measures age gender many in social sciences are unobserved e g achievement behaviors attitudes Levels of Measurement 1 Nominaliindicates classification group 2 Ordinaliindicates rank order 3 Intervaliindicates equal intervals 4 Ratioiindicates nonarbitrary zero higher levels contain more information than lower levels often interested in numerical representation of data although numbers not always easily interpretable e g differences in attitudes often ascribe intervalratio properties to ordinal variables although don t really have those properties e g education levels because of statistical techniques eg linear regression should generally measure at higher level can always collapse to lower level eg age also should weigh difficulty of getting more precise information and how it will be used eg family income IllustrationiNational Household Education Survey A Family demographic information httpncesedgovnhespdfearly99parentpdf B Utility of using established measures 1 generally have been widely pretested and quality scrutinized 2 studies using same measures can be compared eg NELS SES See NELSSS Survey Item report httpncesedgovpubs9797052pdf See handout for additional examples Reliability and validity Reliabilityiability to measure something consistency with repeated measures Validityiability to measure something accurately both important although in some ways reliability more important a measure can be reliable but not valid a measure can t be valid without being reliable ie a reliable measure is still measuring something that may be important e g achievement 1 Measurement error error in measuring something as opposed to sampling error two types systematic measurement errorirelated to the concept being measured due to some identi able reason e g cultural bias investigator bias random erroriunrelated to concept being measured systematic errors affect validity random errors affect reliability and validity see readings page 82 2 Assessing reliability a Testretestitesting same persons on two different occasions problems familiar with test so measures not independent things can change over time b Splithalfidivide items in half examine correlation among two groups problem each pair of items needs to be equivalent c Interrater reliability for observation or archival data soures ways to improve reliability pretest to see if respondents understand questions adding items to composite measure item analysis improve procedures for administering questionnaires 3 Assessing validity a Subjective validation face validityioperational definition seems to represent concept content validityimeasure captures all facets of concept eg reading achievement b Criterionrelated validation examine whether measure predicts related measure of criterion variable achievement test scoresisuccess GPA examine correlation between two measures concurrent validity same time predictive validity different time difficult to first establish predictive validity of criterion variable eg validity of tests as a measure of work performance c Construct validation examine whether measure predicts another measure of related concept examine correlation between two related measures 111 Questionnaire Design A Overview of process Different people involved in process have different roles Researcher formulates research questions identifies concepts and variables creates questionsiwhat exactly does researcher want respondents to do analyzes responses Interviewer administers questionnaire in telephone and facetoface must not bias resultsiprovide consistent information Respondent must understand the question know the answeriinvolves memory be willing to provide answer Steps involves several steps each important process is a craft not easily learned given little attention in graduate training major steps writing questions formatting questionnaire write introductory material mail letter telephone introduction pretesting revising B Writing Questions Principals of writing good questions 1 Simple language simple common language understood by all respondents short tothepoint avoid doublebarreled questions two things to judge double negatives 2 Clear specific terminology see Fowler article precise working eg age at what time 4 put response alternatives after questions or statements de ne terminology especially for concepts that have multiple meanings e g hospitalization breakfast heavy traf c etc Response categories openended versus closeended questions open better when a not enough known to provide answer use in pretest b sensitive topic eg drinking proper response categories covers all possible responses how many categories keep small depends on useialso need variability should provide don t know or no opinion option Culturally appropriate questions Types of questions 1 Factual questions characteristics behaviors problems don t understand the question don t know the answer can t recall inaccurate answerieg NELS absenteeism don t what to report it what can you do about it simple clear de ned terms eliminate question ask someone who knows provide stimulus other questions set time boundaries provide con dential anonymous way of reporting Subjective questions attitudes clear wording proper response categories issue direction of attitudes vs strength of attitudes forced choice questions don t use agreedisagree responseibias due to acquiescence Multiple questions may be necessary to assess complex notions eg crime police courts prisons SES education income Screening questionsiused to determine eligibility for study want to keep Borrowing questions versus designing your own Borrowing Can bene t from previous design work pretest validity reliability 5 v Jj i39 Vila DCT DISCRETE COSINE TRANSFORM N171 1v 71 7T 1 7r 1 ka m M rm Cos m E 12 cos E n E 122 Used To decompose The spa l39ial frequency of image in Terms of various cosines Widely used in various image compression schemes MAT PROJECT UseofDCTTo CalculaTe Frequency complexi ry of image Group images wi rh similar frequency componen rs Demosof Progressive JPEG Frequency seecTive image recons rruc rion FREQUENCY COMPONENTS PROGRESSIVE J PEG ReconsTr ucT The image using frequency componenTs successively Used To Transfer images over a period of Time wiTh iniTial qualify To below and qualify impr ovememL over39 Time PROGRESSIVE JPEG Fveq compunents used FREQUENCY COMPLEXITY Reconstruct image using 7 iow frequency components 7 successiveiy add m high frequency components Mean Difference Error MDE mean of pixel wise difference of actual and reconstructed irna e Check MDE lt quality Threshold v No of iterations required to achieve the v is Frequency complexity EXAMPLE TO FIND FREQ ENCY COMPLEXITY Tabe of various MDE 03609 0 CDVO O39lPOUN IMPORTANCE OF FREQUENCY COMPLEXITY Gives an es rima re of how much frequency con ren r an image has Gives an es rima re of how much compression can be achieved for an image for a quali ry Threshold THUMB RULE lower frequency complexi ry gt higher compression ra rio achievable 4 FREQUENCY COMPLEXITY Which is more complex g FREQUENCY COMPLEXITY Which one is more complex 51 3112 29 71 l f 39 GROUPING OF IMAOES WI SIMILAR FREQUENCY COMPLEXITY Concepf Group similar images for eg Beach scenes foresf scenes efc based on Their frequency confenf Approach Compare high frequency componenfs Reason Low frequency componen rs do no r have much defails LOw FREQUENCY COMPARISON Low Freq componenfs are same buf images GROUPING OF IMAGES Goal To group The following Two pic rur39es inTo one caTegor39y GROUPING OF IMAGES Combine These Two images Too in one coTegory ALGORITHM GeT 8x8 DCT of an images Compare IasT 63 AC componenT of The DCT of The images Check wheTher difference is below cerTain Threshold YES gt Group The images NO gt Move ahead wiTh nexT image W ALGORITHM LOOPHOLES Gives improper resulTs for images ThaT do noT have high frequency complexiTy For These Two figures of same video The difference in freq componenTs is large ALGORITHM CONSTRAINTS Gives perfect results for images that have high frequency complexity For these two figures of same video the difference in freq components is very small WAYS TO AVOID LOOPHOLE Determine the block that should be considered for com arison based on t e average value of the 39 39 block But this needs extensive testing before RESULT WITH MULTIPLE IMAGES IMAGES USED FOR THIS EXPT CONCLUSION DCT is a very important tool for media processing especially images DCT can be used for various applications that we discussed Lower frequency complexity gt Higher compression achievable Higher frequency complexity gt Better grouping possible W


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