SlideShare une entreprise Scribd logo
1  sur  37
Educational Research
Chapter 5
Selecting Measuring Instruments
Gay, Mills, and Airasian
Topics Discussed in this Chapter
 Data collection
 Measuring instruments
 Terminology
 Interpreting data
 Types of instruments
 Technical issues
 Validity
 Reliability
 Selection of a test
Data Collection
 Scientific inquiry requires the collection,
analysis, and interpretation of data
 Data – the pieces of information that are
collected to examine the research topic
 Issues related to the collection of this
information are the focus of this chapter
Data Collection
 Terminology related to data
 Constructs – abstractions that cannot be
observed directly but are helpful when
trying to explain behavior

Intelligence

Teacher effectiveness

Self concept
Obj. 1.1 & 1.2
Data Collection
 Data terminology (continued)
 Operational definition – the ways by which
constructs are observed and measured

Weschler IQ test

Virgilio Teacher Effectiveness Inventory

Tennessee Self-Concept Scale
 Variable – a construct that has been
operationalized and has two or more
values
Obj. 1.1 & 1.2
Data Collection
 Measurement scales
 Nominal – categories

Gender, ethnicity, etc.
 Ordinal – ordered categories

Rank in class, order of finish, etc.
 Interval – equal intervals

Test scores, attitude scores, etc.
 Ratio – absolute zero

Time, height, weight, etc.
Obj. 2.1
Data Collection
 Types of variables
 Categorical or quantitative

Categorical variables reflect nominal scales
and measure the presence of different qualities
(e.g., gender, ethnicity, etc.)

Quantitative variables reflect ordinal, interval, or
ratio scales and measure different quantities of
a variable (e.g., test scores, self-esteem
scores, etc.)
Obj. 2.2
Data Collection
 Types of variables
 Independent or dependent

Independent variables are purported causes

Dependent variables are purported effects

Two instructional strategies, co-operative groups and
traditional lectures, were used during a three week social
studies unit. Students’ exam scores were analyzed for
differences between the groups.
 The independent variable is the instructional approach (of
which there are two levels)
 The dependent variable is the students’ achievement
Obj. 2.3
Measurement Instruments
 Important terms
 Instrument – a tool used to collect data
 Test – a formal, systematic procedure for
gathering information
 Assessment – the general process of
collecting, synthesizing, and interpreting
information
 Measurement – the process of quantifying
or scoring a subject’s performance
Obj. 3.1 & 3.2
Measurement Instruments
 Important terms (continued)
 Cognitive tests – examining subjects’
thoughts and thought processes
 Affective tests – examining subjects’
feelings, interests, attitudes, beliefs, etc.
 Standardized tests – tests that are
administered, scored, and interpreted in a
consistent manner
Obj. 3.1
Measurement Instruments
 Important terms (continued)
 Selected response item format – respondents
select answers from a set of alternatives

Multiple choice

True-false

Matching
 Supply response item format – respondents
construct answers

Short answer

Completion

Essay
Obj. 3.3 & 11.3
Measurement Instruments
 Important terms (continued)
 Individual tests – tests administered on an
individual basis
 Group tests – tests administered to a group
of subjects at the same time
 Performance assessments – assessments
that focus on processes or products that
have been created
Obj. 3.6
Measurement Instruments
 Interpreting data
 Raw scores – the actual score made on a
test
 Standard scores – statistical
transformations of raw scores

Percentiles (0.00 – 99.9)

Stanines (1 – 9)

Normal Curve Equivalents (0.00 – 99.99)
Obj. 3.4
Measurement Instruments
 Interpreting data (continued)
 Norm-referenced – scores are interpreted
relative to the scores of others taking the
test
 Criterion-referenced – scores are
interpreted relative to a predetermined
level of performance
 Self-referenced – scores are interpreted
relative to changes over time
Obj. 3.5
Measurement Instruments
 Types of instruments
 Cognitive – measuring intellectual
processes such as thinking, memorizing,
problem solving, analyzing, or reasoning
 Achievement – measuring what students
already know
 Aptitude – measuring general mental
ability, usually for predicting future
performance
Obj. 4.1 & 4.2
Measurement Instruments
 Types of instruments (continued)
 Affective – assessing individuals’ feelings,
values, attitudes, beliefs, etc.

Typical affective characteristics of interest
 Values – deeply held beliefs about ideas, persons, or
objects
 Attitudes – dispositions that are favorable or unfavorable
toward things
 Interests – inclinations to seek out or participate in
particular activities, objects, ideas, etc.
 Personality – characteristics that represent a person’s
typical behaviors
Obj. 4.1 & 4.5
Measurement Instruments
 Types of instruments (continued)
 Affective (continued)

Scales used for responding to items on affective tests
 Likert

Positive or negative statements to which subjects
respond on scales such as strongly disagree,
disagree, neutral, agree, or strongly agree
 Semantic differential

Bipolar adjectives (i.e., two opposite adjectives) with a
scale between each adjective

Dislike: ___ ___ ___ ___ ___ :Like
 Rating scales – rankings based on how a subject would
rate the trait of interest
Obj. 5.1
Measurement Instruments
 Types of instruments (continued)
 Affective (continued)

Scales used for responding to items on
affective tests (continued)
 Thurstone – statements related to the trait of interest
to which subjects agree or disagree
 Guttman – statements representing a uni-
dimensional trait
Obj. 5.1
Measurement Instruments
 Issues for cognitive, aptitude, or affective
tests
 Problems inherent in the use of self-report
measures

Bias – distortions of a respondent’s performance or
responses based on ethnicity, race, gender, language,
etc.

Responses to affective test items
 Socially acceptable responses
 Accuracy of responses
 Response sets
 Alternatives include the use of projective tests
Obj. 4.3, 4.4
Technical Issues
 Two concerns
 Validity
 Reliability
Technical Issues
 Validity – extent to which interpretations
made from a test score are appropriate
 Characteristics

The most important technical characteristic

Situation specific

Does not refer to the instrument but to the
interpretations of scores on the instrument

Best thought of in terms of degree
Obj. 6.1 & 7.1
Technical Issues
 Validity (continued)
 Four types

Content – to what extent does the test measure
what it is supposed to measure
 Item validity
 Sampling validity
 Determined by expert judgment
Obj. 7.1 & 7.2
Technical Issues
 Validity (continued)

Criterion-related
 Predictive – to what extent does the test predict a
future performance
 Concurrent - to what extent does the test predict a
performance measured at the same time
 Estimated by correlations between two tests

Construct – the extent to which a test measures
the construct it represents
 Underlying difficulty defining constructs
 Estimated in many ways
Obj. 7.1, 7.3, & 7.4
Technical Issues
 Validity (continued)
 Consequential – to what extent are the
consequences that occur from the test
harmful

Estimated by empirical and expert judgment
 Factors affecting validity

Unclear test directions

Confusing and ambiguous test items

Vocabulary that is too difficult for test takers
Obj. 7.1, 7.5, & 7.7
Technical Issues
 Factors affecting validity (continued)
 Overly difficult and complex sentence
structure
 Inconsistent and subjective scoring
 Untaught items
 Failure to follow standardized
administration procedures
 Cheating by the participants or someone
teaching to the test items
Obj. 7.7
Technical Issues
 Reliability – the degree to which a test
consistently measures whatever it is
measuring
 Characteristics

Expressed as a coefficient ranging from 0 to 1

A necessary but not sufficient characteristic of
a test
Obj. 6.1, 8.1, & 8.7
Technical Issues
 Reliability (continued)
 Six reliability coefficients

Stability – consistency over time with the same
instrument
 Test – retest
 Estimated by a correlation between the two
administrations of the same test

Equivalence – consistency with two parallel
tests administered at the same time
 Parallel forms
 Estimated by a correlation between the parallel tests
Obj. 8.1, 8.2, 8.3, & 8.7
Technical Issues
 Reliability (continued)
 Six reliability coefficients (continued)

Equivalence and stability – consistency over
time with parallel forms of the test
 Combines attributes of stability and equivalence
 Estimated by a correlation between the parallel forms

Internal consistency – artificially splitting the
test into halves
 Several coefficients – split halves, KR 20, KR 21, Cronbach
alpha
 All coefficients provide estimates ranging from 0 to 1
Obj. 8.1, 8.4, 8.5, & 8.7
Technical Issues
 Reliability (continued)
 Six reliability coefficients

Scorer/rater – consistency of observations
between raters
 Inter-judge – two observers
 Intra-judge – one judge over two occasions
 Estimated by percent agreement between
observations
Obj. 8.1, 8.6, & 8.7
Technical Issues
 Reliability (continued)
 Six reliability coefficients (continued)

Standard error of measurement (SEM) – an
estimate of how much difference there is
between a person’s obtained score and his or
her true score
 Function of the variation of the test and the reliability
coefficient (e.g., KR 20, Cronbach alpha, etc.)
 Estimated by specifying an interval rather than a
point estimate of a person’s score
Obj. 8.1, 8.7, & 9.1
Selection of a Test
 Sources of test information
 Mental Measurement Yearbooks (MMY)

The reviews in MMY are most easily accessed through
your university library and the services to which they
subscribe (e.g., EBSCO)

Provides factual information on all known tests

Provides objective test reviews

Comprehensive bibliography for specific tests

Indices: titles, acronyms, subject, publishers, developers
 Buros Institute
Obj. 10.1 & 12.1
Selection of a Test
 Sources (continued)
 Tests in Print

Tests in Print is a subsidiary of the Buros Institute

The reviews in it are most easily accessed through your
university library and the services to which they
subscribe (e.g., EBSCO)

Bibliography of all known commercially produced tests
currently available

Very useful to determine availability
 Tests in Print
Obj. 10.1 & 12.1
Selection of a Test
 Sources (continued)
 ETS Test Collection

Published and unpublished tests

Includes test title, author, publication date, target
population, publisher, and description of purpose

Annotated bibliographies on achievement, aptitude,
attitude and interests, personality, sensory motor, special
populations, vocational/occupational, and miscellaneous
 ETS Test Collection
Obj. 10.1 &12.1
Selection of a Test
 Sources (continued)
 Professional journals
 Test publishers and distributors
 Issues to consider when selecting tests
 Psychometric properties

Validity

Reliability

Length of test

Scoring and score interpretation
Obj. 10.1, 11.1, & 12.1
Selection of a Test
 Issues to consider when selecting tests
 Non-psychometric issues

Cost

Administrative time

Objections to content by parents or others

Duplication of testing
Obj. 11.1
Selection of a Test
 Designing your own tests
 Get help from others with experience in
developing tests
 Item writing guidelines

Avoid ambiguous and confusing wording and sentence
structure

Use appropriate vocabulary

Write items that have only one correct answer

Give information about the nature of the desired answer

Do not provide clues to the correct answer
 See Writing Multiple Choice Items
Obj. 11.2
Selection of a Test
 Test administration guidelines
 Plan ahead
 Be certain that there is consistency across
testing sessions
 Be familiar with any and all procedures
necessary to administer a test
Obj. 11.4

Contenu connexe

Tendances

TSL3133 Topic 11 Qualitative Data Analysis
TSL3133 Topic 11 Qualitative Data AnalysisTSL3133 Topic 11 Qualitative Data Analysis
TSL3133 Topic 11 Qualitative Data AnalysisYee Bee Choo
 
Steps for Preparing Research Methodology - Phdassistance
Steps for Preparing Research Methodology - PhdassistanceSteps for Preparing Research Methodology - Phdassistance
Steps for Preparing Research Methodology - PhdassistancePhD Assistance
 
Developing instruments for research
Developing instruments for researchDeveloping instruments for research
Developing instruments for researchCarlo Magno
 
Notes from frankel and wallen
Notes from frankel  and wallenNotes from frankel  and wallen
Notes from frankel and wallenRose Jedin
 
Live innovation.org 4.-defining-the-research-plan-research-design-mind-map
Live innovation.org 4.-defining-the-research-plan-research-design-mind-mapLive innovation.org 4.-defining-the-research-plan-research-design-mind-map
Live innovation.org 4.-defining-the-research-plan-research-design-mind-mapsimran kaur
 
Rigour & robustness in research 16 april 2015
Rigour & robustness in research 16 april 2015Rigour & robustness in research 16 april 2015
Rigour & robustness in research 16 april 2015Dr. Jennifer Loke
 
Research process
Research processResearch process
Research processRupesh Nath
 
Mixed method research
Mixed method researchMixed method research
Mixed method researchNaveen Pareek
 
Research and research process
Research and research processResearch and research process
Research and research processAtul Yadav
 
Mixed Methods Research Design
Mixed Methods Research DesignMixed Methods Research Design
Mixed Methods Research DesignSYIKIN MARIA
 
How to write the methodology chapter of a dissertation or thesis
How to write the methodology chapter of a dissertation or thesisHow to write the methodology chapter of a dissertation or thesis
How to write the methodology chapter of a dissertation or thesisRuwin Dias
 
Research Questions and Hypotheses
Research Questions and Hypotheses Research Questions and Hypotheses
Research Questions and Hypotheses Pedro Martinez
 

Tendances (20)

TSL3133 Topic 11 Qualitative Data Analysis
TSL3133 Topic 11 Qualitative Data AnalysisTSL3133 Topic 11 Qualitative Data Analysis
TSL3133 Topic 11 Qualitative Data Analysis
 
Steps for Preparing Research Methodology - Phdassistance
Steps for Preparing Research Methodology - PhdassistanceSteps for Preparing Research Methodology - Phdassistance
Steps for Preparing Research Methodology - Phdassistance
 
Developing instruments for research
Developing instruments for researchDeveloping instruments for research
Developing instruments for research
 
Notes from frankel and wallen
Notes from frankel  and wallenNotes from frankel  and wallen
Notes from frankel and wallen
 
Research proposal
Research proposalResearch proposal
Research proposal
 
Tools of Education Research- Dr. K. Thiyagu
Tools of Education Research- Dr. K. ThiyaguTools of Education Research- Dr. K. Thiyagu
Tools of Education Research- Dr. K. Thiyagu
 
Live innovation.org 4.-defining-the-research-plan-research-design-mind-map
Live innovation.org 4.-defining-the-research-plan-research-design-mind-mapLive innovation.org 4.-defining-the-research-plan-research-design-mind-map
Live innovation.org 4.-defining-the-research-plan-research-design-mind-map
 
Rigour & robustness in research 16 april 2015
Rigour & robustness in research 16 april 2015Rigour & robustness in research 16 april 2015
Rigour & robustness in research 16 april 2015
 
Research process
Research processResearch process
Research process
 
Research problem
Research problemResearch problem
Research problem
 
Econ
EconEcon
Econ
 
Mixed method research
Mixed method researchMixed method research
Mixed method research
 
Research and research process
Research and research processResearch and research process
Research and research process
 
Mixed Methods Research Design
Mixed Methods Research DesignMixed Methods Research Design
Mixed Methods Research Design
 
Research methodology
Research methodologyResearch methodology
Research methodology
 
Research problem
Research problemResearch problem
Research problem
 
Researchproblem
ResearchproblemResearchproblem
Researchproblem
 
How to write the methodology chapter of a dissertation or thesis
How to write the methodology chapter of a dissertation or thesisHow to write the methodology chapter of a dissertation or thesis
How to write the methodology chapter of a dissertation or thesis
 
Mixed Method Research
Mixed Method ResearchMixed Method Research
Mixed Method Research
 
Research Questions and Hypotheses
Research Questions and Hypotheses Research Questions and Hypotheses
Research Questions and Hypotheses
 

En vedette

Educational research need concept and types by purpose
Educational research need concept and types by purposeEducational research need concept and types by purpose
Educational research need concept and types by purposeInam Jahangir
 
Website research Wiz Khalifa
Website research Wiz KhalifaWebsite research Wiz Khalifa
Website research Wiz Khalifabeesonjack
 
Sample proposal budgets
Sample proposal budgetsSample proposal budgets
Sample proposal budgetsDon Don
 
Intro to research methodology 2
Intro to research methodology 2Intro to research methodology 2
Intro to research methodology 2Zhiqz Dee
 
Qualitative & Quantitative Research Method
Qualitative & Quantitative Research MethodQualitative & Quantitative Research Method
Qualitative & Quantitative Research Methoditsgeedoe
 
Ch1 introduction to educational research
Ch1 introduction to educational researchCh1 introduction to educational research
Ch1 introduction to educational researchsaima sardar
 
15 free qualitative and quantitative research methods books
15 free qualitative and quantitative research methods books15 free qualitative and quantitative research methods books
15 free qualitative and quantitative research methods booksThe Free School
 
Educational research
Educational researchEducational research
Educational researchmeenuch
 
Quantitative And Qualitative Research
Quantitative And Qualitative ResearchQuantitative And Qualitative Research
Quantitative And Qualitative Researchdoha07
 
Qualitative and quantitative methods of research
Qualitative and quantitative methods of researchQualitative and quantitative methods of research
Qualitative and quantitative methods of researchJordan Cruz
 

En vedette (15)

Educational Research
Educational ResearchEducational Research
Educational Research
 
Educational research need concept and types by purpose
Educational research need concept and types by purposeEducational research need concept and types by purpose
Educational research need concept and types by purpose
 
Ch02 topic selection
Ch02 topic selectionCh02 topic selection
Ch02 topic selection
 
How to
How toHow to
How to
 
Website research Wiz Khalifa
Website research Wiz KhalifaWebsite research Wiz Khalifa
Website research Wiz Khalifa
 
Sample proposal budgets
Sample proposal budgetsSample proposal budgets
Sample proposal budgets
 
Intro to research methodology 2
Intro to research methodology 2Intro to research methodology 2
Intro to research methodology 2
 
Qualitative & Quantitative Research Method
Qualitative & Quantitative Research MethodQualitative & Quantitative Research Method
Qualitative & Quantitative Research Method
 
Ppt
PptPpt
Ppt
 
Ch12 (1)
Ch12 (1)Ch12 (1)
Ch12 (1)
 
Ch1 introduction to educational research
Ch1 introduction to educational researchCh1 introduction to educational research
Ch1 introduction to educational research
 
15 free qualitative and quantitative research methods books
15 free qualitative and quantitative research methods books15 free qualitative and quantitative research methods books
15 free qualitative and quantitative research methods books
 
Educational research
Educational researchEducational research
Educational research
 
Quantitative And Qualitative Research
Quantitative And Qualitative ResearchQuantitative And Qualitative Research
Quantitative And Qualitative Research
 
Qualitative and quantitative methods of research
Qualitative and quantitative methods of researchQualitative and quantitative methods of research
Qualitative and quantitative methods of research
 

Similaire à Selecting Measuring Instruments for Educational Research

Validity and reliability in assessment.
Validity and reliability in assessment. Validity and reliability in assessment.
Validity and reliability in assessment. Tarek Tawfik Amin
 
research-instruments (1).pptx
research-instruments (1).pptxresearch-instruments (1).pptx
research-instruments (1).pptxJCronus
 
Louzel Report - Reliability & validity
Louzel Report - Reliability & validity Louzel Report - Reliability & validity
Louzel Report - Reliability & validity Louzel Linejan
 
Construction of Tests
Construction of TestsConstruction of Tests
Construction of TestsDakshta1
 
Quantitative measurement
Quantitative measurementQuantitative measurement
Quantitative measurementCarla Piper
 
Validity and reliability of questionnaires
Validity and reliability of questionnairesValidity and reliability of questionnaires
Validity and reliability of questionnairesVenkitachalam R
 
Principles of quantitative research
Principles of quantitative researchPrinciples of quantitative research
Principles of quantitative researchBurhan Hadi
 
Validity and reliability
Validity and reliabilityValidity and reliability
Validity and reliabilityrandoparis
 
Assessing learning in Instructional Design
Assessing learning in Instructional DesignAssessing learning in Instructional Design
Assessing learning in Instructional Designleesha roberts
 
PRINCIPLES OF ASSESSMENT 2.pptx
PRINCIPLES OF ASSESSMENT 2.pptxPRINCIPLES OF ASSESSMENT 2.pptx
PRINCIPLES OF ASSESSMENT 2.pptxJoelGuamani2
 
Test development
Test developmentTest development
Test developmentZubair Khan
 
Week 9 validity and reliability
Week 9 validity and reliabilityWeek 9 validity and reliability
Week 9 validity and reliabilitywawaaa789
 
Characteristics of a good test
Characteristics of a good testCharacteristics of a good test
Characteristics of a good testALMA HERMOGINO
 
JC-16-23June2021-rel-val.pptx
JC-16-23June2021-rel-val.pptxJC-16-23June2021-rel-val.pptx
JC-16-23June2021-rel-val.pptxsaurami
 

Similaire à Selecting Measuring Instruments for Educational Research (20)

Validity and reliability in assessment.
Validity and reliability in assessment. Validity and reliability in assessment.
Validity and reliability in assessment.
 
research-instruments (1).pptx
research-instruments (1).pptxresearch-instruments (1).pptx
research-instruments (1).pptx
 
Week 8 & 9 - Validity and Reliability
Week 8 & 9 - Validity and ReliabilityWeek 8 & 9 - Validity and Reliability
Week 8 & 9 - Validity and Reliability
 
Louzel Report - Reliability & validity
Louzel Report - Reliability & validity Louzel Report - Reliability & validity
Louzel Report - Reliability & validity
 
Construction of Tests
Construction of TestsConstruction of Tests
Construction of Tests
 
Quantitative measurement
Quantitative measurementQuantitative measurement
Quantitative measurement
 
Validity and reliability of questionnaires
Validity and reliability of questionnairesValidity and reliability of questionnaires
Validity and reliability of questionnaires
 
Principles of quantitative research
Principles of quantitative researchPrinciples of quantitative research
Principles of quantitative research
 
Lecture 7 measurement ii
Lecture   7   measurement iiLecture   7   measurement ii
Lecture 7 measurement ii
 
Rree measurement-larry-d3
Rree measurement-larry-d3Rree measurement-larry-d3
Rree measurement-larry-d3
 
Validity and reliability
Validity and reliabilityValidity and reliability
Validity and reliability
 
7.1 assessment and the cefr (1)
7.1 assessment and the cefr (1)7.1 assessment and the cefr (1)
7.1 assessment and the cefr (1)
 
Assessing learning in Instructional Design
Assessing learning in Instructional DesignAssessing learning in Instructional Design
Assessing learning in Instructional Design
 
PRINCIPLES OF ASSESSMENT 2.pptx
PRINCIPLES OF ASSESSMENT 2.pptxPRINCIPLES OF ASSESSMENT 2.pptx
PRINCIPLES OF ASSESSMENT 2.pptx
 
Quantitative Research
Quantitative ResearchQuantitative Research
Quantitative Research
 
Test development
Test developmentTest development
Test development
 
Week 9 validity and reliability
Week 9 validity and reliabilityWeek 9 validity and reliability
Week 9 validity and reliability
 
7.1 assessment and the cefr (1)
7.1 assessment and the cefr (1)7.1 assessment and the cefr (1)
7.1 assessment and the cefr (1)
 
Characteristics of a good test
Characteristics of a good testCharacteristics of a good test
Characteristics of a good test
 
JC-16-23June2021-rel-val.pptx
JC-16-23June2021-rel-val.pptxJC-16-23June2021-rel-val.pptx
JC-16-23June2021-rel-val.pptx
 

Dernier

"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024The Digital Insurer
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 

Dernier (20)

"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 

Selecting Measuring Instruments for Educational Research

  • 1. Educational Research Chapter 5 Selecting Measuring Instruments Gay, Mills, and Airasian
  • 2. Topics Discussed in this Chapter  Data collection  Measuring instruments  Terminology  Interpreting data  Types of instruments  Technical issues  Validity  Reliability  Selection of a test
  • 3. Data Collection  Scientific inquiry requires the collection, analysis, and interpretation of data  Data – the pieces of information that are collected to examine the research topic  Issues related to the collection of this information are the focus of this chapter
  • 4. Data Collection  Terminology related to data  Constructs – abstractions that cannot be observed directly but are helpful when trying to explain behavior  Intelligence  Teacher effectiveness  Self concept Obj. 1.1 & 1.2
  • 5. Data Collection  Data terminology (continued)  Operational definition – the ways by which constructs are observed and measured  Weschler IQ test  Virgilio Teacher Effectiveness Inventory  Tennessee Self-Concept Scale  Variable – a construct that has been operationalized and has two or more values Obj. 1.1 & 1.2
  • 6. Data Collection  Measurement scales  Nominal – categories  Gender, ethnicity, etc.  Ordinal – ordered categories  Rank in class, order of finish, etc.  Interval – equal intervals  Test scores, attitude scores, etc.  Ratio – absolute zero  Time, height, weight, etc. Obj. 2.1
  • 7. Data Collection  Types of variables  Categorical or quantitative  Categorical variables reflect nominal scales and measure the presence of different qualities (e.g., gender, ethnicity, etc.)  Quantitative variables reflect ordinal, interval, or ratio scales and measure different quantities of a variable (e.g., test scores, self-esteem scores, etc.) Obj. 2.2
  • 8. Data Collection  Types of variables  Independent or dependent  Independent variables are purported causes  Dependent variables are purported effects  Two instructional strategies, co-operative groups and traditional lectures, were used during a three week social studies unit. Students’ exam scores were analyzed for differences between the groups.  The independent variable is the instructional approach (of which there are two levels)  The dependent variable is the students’ achievement Obj. 2.3
  • 9. Measurement Instruments  Important terms  Instrument – a tool used to collect data  Test – a formal, systematic procedure for gathering information  Assessment – the general process of collecting, synthesizing, and interpreting information  Measurement – the process of quantifying or scoring a subject’s performance Obj. 3.1 & 3.2
  • 10. Measurement Instruments  Important terms (continued)  Cognitive tests – examining subjects’ thoughts and thought processes  Affective tests – examining subjects’ feelings, interests, attitudes, beliefs, etc.  Standardized tests – tests that are administered, scored, and interpreted in a consistent manner Obj. 3.1
  • 11. Measurement Instruments  Important terms (continued)  Selected response item format – respondents select answers from a set of alternatives  Multiple choice  True-false  Matching  Supply response item format – respondents construct answers  Short answer  Completion  Essay Obj. 3.3 & 11.3
  • 12. Measurement Instruments  Important terms (continued)  Individual tests – tests administered on an individual basis  Group tests – tests administered to a group of subjects at the same time  Performance assessments – assessments that focus on processes or products that have been created Obj. 3.6
  • 13. Measurement Instruments  Interpreting data  Raw scores – the actual score made on a test  Standard scores – statistical transformations of raw scores  Percentiles (0.00 – 99.9)  Stanines (1 – 9)  Normal Curve Equivalents (0.00 – 99.99) Obj. 3.4
  • 14. Measurement Instruments  Interpreting data (continued)  Norm-referenced – scores are interpreted relative to the scores of others taking the test  Criterion-referenced – scores are interpreted relative to a predetermined level of performance  Self-referenced – scores are interpreted relative to changes over time Obj. 3.5
  • 15. Measurement Instruments  Types of instruments  Cognitive – measuring intellectual processes such as thinking, memorizing, problem solving, analyzing, or reasoning  Achievement – measuring what students already know  Aptitude – measuring general mental ability, usually for predicting future performance Obj. 4.1 & 4.2
  • 16. Measurement Instruments  Types of instruments (continued)  Affective – assessing individuals’ feelings, values, attitudes, beliefs, etc.  Typical affective characteristics of interest  Values – deeply held beliefs about ideas, persons, or objects  Attitudes – dispositions that are favorable or unfavorable toward things  Interests – inclinations to seek out or participate in particular activities, objects, ideas, etc.  Personality – characteristics that represent a person’s typical behaviors Obj. 4.1 & 4.5
  • 17. Measurement Instruments  Types of instruments (continued)  Affective (continued)  Scales used for responding to items on affective tests  Likert  Positive or negative statements to which subjects respond on scales such as strongly disagree, disagree, neutral, agree, or strongly agree  Semantic differential  Bipolar adjectives (i.e., two opposite adjectives) with a scale between each adjective  Dislike: ___ ___ ___ ___ ___ :Like  Rating scales – rankings based on how a subject would rate the trait of interest Obj. 5.1
  • 18. Measurement Instruments  Types of instruments (continued)  Affective (continued)  Scales used for responding to items on affective tests (continued)  Thurstone – statements related to the trait of interest to which subjects agree or disagree  Guttman – statements representing a uni- dimensional trait Obj. 5.1
  • 19. Measurement Instruments  Issues for cognitive, aptitude, or affective tests  Problems inherent in the use of self-report measures  Bias – distortions of a respondent’s performance or responses based on ethnicity, race, gender, language, etc.  Responses to affective test items  Socially acceptable responses  Accuracy of responses  Response sets  Alternatives include the use of projective tests Obj. 4.3, 4.4
  • 20. Technical Issues  Two concerns  Validity  Reliability
  • 21. Technical Issues  Validity – extent to which interpretations made from a test score are appropriate  Characteristics  The most important technical characteristic  Situation specific  Does not refer to the instrument but to the interpretations of scores on the instrument  Best thought of in terms of degree Obj. 6.1 & 7.1
  • 22. Technical Issues  Validity (continued)  Four types  Content – to what extent does the test measure what it is supposed to measure  Item validity  Sampling validity  Determined by expert judgment Obj. 7.1 & 7.2
  • 23. Technical Issues  Validity (continued)  Criterion-related  Predictive – to what extent does the test predict a future performance  Concurrent - to what extent does the test predict a performance measured at the same time  Estimated by correlations between two tests  Construct – the extent to which a test measures the construct it represents  Underlying difficulty defining constructs  Estimated in many ways Obj. 7.1, 7.3, & 7.4
  • 24. Technical Issues  Validity (continued)  Consequential – to what extent are the consequences that occur from the test harmful  Estimated by empirical and expert judgment  Factors affecting validity  Unclear test directions  Confusing and ambiguous test items  Vocabulary that is too difficult for test takers Obj. 7.1, 7.5, & 7.7
  • 25. Technical Issues  Factors affecting validity (continued)  Overly difficult and complex sentence structure  Inconsistent and subjective scoring  Untaught items  Failure to follow standardized administration procedures  Cheating by the participants or someone teaching to the test items Obj. 7.7
  • 26. Technical Issues  Reliability – the degree to which a test consistently measures whatever it is measuring  Characteristics  Expressed as a coefficient ranging from 0 to 1  A necessary but not sufficient characteristic of a test Obj. 6.1, 8.1, & 8.7
  • 27. Technical Issues  Reliability (continued)  Six reliability coefficients  Stability – consistency over time with the same instrument  Test – retest  Estimated by a correlation between the two administrations of the same test  Equivalence – consistency with two parallel tests administered at the same time  Parallel forms  Estimated by a correlation between the parallel tests Obj. 8.1, 8.2, 8.3, & 8.7
  • 28. Technical Issues  Reliability (continued)  Six reliability coefficients (continued)  Equivalence and stability – consistency over time with parallel forms of the test  Combines attributes of stability and equivalence  Estimated by a correlation between the parallel forms  Internal consistency – artificially splitting the test into halves  Several coefficients – split halves, KR 20, KR 21, Cronbach alpha  All coefficients provide estimates ranging from 0 to 1 Obj. 8.1, 8.4, 8.5, & 8.7
  • 29. Technical Issues  Reliability (continued)  Six reliability coefficients  Scorer/rater – consistency of observations between raters  Inter-judge – two observers  Intra-judge – one judge over two occasions  Estimated by percent agreement between observations Obj. 8.1, 8.6, & 8.7
  • 30. Technical Issues  Reliability (continued)  Six reliability coefficients (continued)  Standard error of measurement (SEM) – an estimate of how much difference there is between a person’s obtained score and his or her true score  Function of the variation of the test and the reliability coefficient (e.g., KR 20, Cronbach alpha, etc.)  Estimated by specifying an interval rather than a point estimate of a person’s score Obj. 8.1, 8.7, & 9.1
  • 31. Selection of a Test  Sources of test information  Mental Measurement Yearbooks (MMY)  The reviews in MMY are most easily accessed through your university library and the services to which they subscribe (e.g., EBSCO)  Provides factual information on all known tests  Provides objective test reviews  Comprehensive bibliography for specific tests  Indices: titles, acronyms, subject, publishers, developers  Buros Institute Obj. 10.1 & 12.1
  • 32. Selection of a Test  Sources (continued)  Tests in Print  Tests in Print is a subsidiary of the Buros Institute  The reviews in it are most easily accessed through your university library and the services to which they subscribe (e.g., EBSCO)  Bibliography of all known commercially produced tests currently available  Very useful to determine availability  Tests in Print Obj. 10.1 & 12.1
  • 33. Selection of a Test  Sources (continued)  ETS Test Collection  Published and unpublished tests  Includes test title, author, publication date, target population, publisher, and description of purpose  Annotated bibliographies on achievement, aptitude, attitude and interests, personality, sensory motor, special populations, vocational/occupational, and miscellaneous  ETS Test Collection Obj. 10.1 &12.1
  • 34. Selection of a Test  Sources (continued)  Professional journals  Test publishers and distributors  Issues to consider when selecting tests  Psychometric properties  Validity  Reliability  Length of test  Scoring and score interpretation Obj. 10.1, 11.1, & 12.1
  • 35. Selection of a Test  Issues to consider when selecting tests  Non-psychometric issues  Cost  Administrative time  Objections to content by parents or others  Duplication of testing Obj. 11.1
  • 36. Selection of a Test  Designing your own tests  Get help from others with experience in developing tests  Item writing guidelines  Avoid ambiguous and confusing wording and sentence structure  Use appropriate vocabulary  Write items that have only one correct answer  Give information about the nature of the desired answer  Do not provide clues to the correct answer  See Writing Multiple Choice Items Obj. 11.2
  • 37. Selection of a Test  Test administration guidelines  Plan ahead  Be certain that there is consistency across testing sessions  Be familiar with any and all procedures necessary to administer a test Obj. 11.4