Descriptive Causal

7/31/2019 Descriptive Causal

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Research Designs

Five Types of Designs

DescriptiveCausal-Comparative Correlational

True Experimental

Quasi-Experimental



“I keep six honest serving men, (they

taught me all I knew), their names arewhat, and why, and when, and how, and

where and who.”

--Rudyard Kipling



Descriptive Research

Definition

Describes characteristics of apopulation or phenomenon

The who, what, when, where,and how of a situation




Characteristics and Uses

No controlling of variablesSome understanding of the

problem

Current status of subjectduring certain time period




Characteristics and Uses

Develop or redefineeducational methods

Test hypothesis or answerquestions




Examples of Questions

How many teachers hold favorable attitudes toward phonics instruction?

What are the reactions of school principals

to the frequency of school shootings?




Statistics

Measures of central tendencyMean

Mode

Median




Statistics

Measures of VariabilityRangeQuartile Scores

VariancesStandard Deviation




Statistical Uses

GPA and ACT Scores in relationshipto college remediation rates

NAEP Reports on reading, math,

and science instruction




Two Types

Point in Time

Over Time




Point in Time

One time snapshot of what isoccurring

Examples: Opinion Polls of attitudes of

conditions & Behaviors of people in a

particular setting




Over Time (Two Types)

Longitudinal Studies

Cross-Sectional




Longitudinal Studies

Three designs• Trend• Cohort• Panel

Study changes or continuity in

sample’s characteristics at different

points in time




Trend Studies

New sample at each data collectionperiod from certain population that

may change

Example: NAEP’s Reading Study at different grade levels over a period of

several years




Cohort Studies

New sample selected at eachcollection period from a population

that remains constant

Example: Work status of elementary teachers receiving California teaching

certificates in 1992




Panel Studies

Same participants each year fromthe same initial group

Example:National study of 12th grade

students into adulthood that found response to study declined over time




Panel Studies

Problems Associated Note changes in individuals and

investigate and explore why

Self-fulfilling expectations withidentification process, biased over time,

sensitive small changes in population




Cross-Sectional

Data collected at one point in time,but from groups that are in differing

stages of development or age

Example: Student attitudes toward math from 7th to 12th grade taken

during a two week period



Causal-Comparative Research

Definition Ex post facto - looks at causes “after the

fact” Seeks to discover possible cause and

effects of a behavioral pattern or

personal characteristic Focuses on relationships between

variables




Definitions

Variable that “causes” other variable to

change is the independent variable

Independent variable cannot be

manipulated (impossible or not ethical)

Examples: gender, age, social economic

status




Definitions

Variable that is “effected” by the other

variable is the dependent variable

Keyword “difference” (by grouping)

would be a tip-off of an ex post factostudy




Uses and Purposes

Simplest quantitative approach to

relationship study

Allows researchers to find relationships

and the degree of interaction betweengroups in an organized and scientific

manner




Uses and Purposes

Goal is to find some type of event,

treatment, or program that becomes the

cause

Outcome or effect is what we want to

study and know about

Outcome is the research problem




Participant and Variable Givens

Exact knowledge of variables required

Independent and dependent variables

Comparisons of pre-defined groups

No manipulation of variables allowed

No random assignment of individuals or groups

Degree of causal-effect is uncertain




Advantages

Allows cause-effect relationship study

when subject manipulation is impossible

or very difficult

Allows for study of many relationships

within a given population at one time




Problem

Degree at which a researcher can

determine the causal-effect with any

measure of certainty

In relationship study of X and Y, may

reveal X causes Y, Y causes X, or anothervariable Z cause both X and Y.




Example:

Study of 477 high school students and

the effect on their academic

achievement due to part-time

employment

Third variable came into play-

extracurricular activities




Study Design

Problem Statement

Defined Group

Comparison Group (two approaches)

Data Collection Techniques

Data Analysis




Problem Statement

Speculation from observations,

previous research, or studied theory

Address and test alternative

hypothesis occurring between twogroups or variables ( strong inference)




Problem Statement

Example

Study speculated that junior high

students’ ability to become literate in

science might depend upon theirteachers’ verbal and written

instructional methods




Defined Group

Group must be defined and possess the

characteristic to be studied

Definition must be precise for

understanding and interpretation of findings




Defined Group

Example

Science literacy study defined one

teacher population as being “high

context-enriched” in their instructionand the other as “low context -enriched”




Comparison Groups

Select group not possessing or not to the

same degree, the defined characteristic

Usually has similar characteristics with

exception of the variable that is the focus of the study




Comparison Groups

Initial study may reveal that the two groups

differ significantly on another variable

Two techniques maybe used to create new

subgroups to eliminate the third variable

– Matching and Extreme Grouping




Comparison Groups

Matching

Example

Each high-context-use teacher

matched to low-context-use teacherwith similar years experience




Comparison Groups

Extreme Groups

Example

100 teachers’ classes studied in regards to references of science context. Students score

each teacher and the 10 scoring highest and the 10 scoring lowest make up the two study groups.




Data Collection

Observational dataInterview information

Questionnaire data

Standardized test scores




Data Collection

Multitude of measurements can ensure

that the research is assessing all

relationships that may exist between

the presumed cause and the presumedeffect




Data Collection

Example

Science literacy study researchers administered several instruments to students to measure

many student learning outcomes, looking at

content, knowledge of subject, processes in science, and historical development




Data Analysis Example

Teacher's use of context-enriched statements assigned percentage of time in teachingscores, students’ scores were measured

scores on attitude and aptitude instrumentswith the mean and standard deviation computed for each set of student-teacher scores




Various Significance Test

t Test for Difference Between Means

– Test significance of difference in two sample

means

– Assume scores form an interval or ratio scale,

normal distribution, and score variance forpopulation is equal

– t test for independent means used if matching

variable is not available





t Test for Single Mean

– Test to see if single mean of sample

differs significantly form the

specified population mean





Analysis of Variance (ANOVA)

– When more than two groups are studied

– Keeps researcher from doing many t test

– Compares the amount of between-groupvariance in individual scores with the amount

of variance within group scores





t Test for Multiple Comparisons

– Test of significance of the differences between

two population means

– Done after ANOVA to show significant

difference between group scores and individual

scores




Parametric Test

Assumptions about populationparameters, the shape or varianceof the population scores –

Analysis of Covariance (ANCOVA) – Multivariate Analysis of Variance

(MANOVA)




Analysis of Covariance (ANCOVA)

Used to determine whether a difference

between two groups on a particularvariable can be explained by anotherdifference between the two

Used to control the initial difference Effect is to make two groups equal with

respect to one or more control variables




Multivariate Analysis of Variance (MANOVA)

Used to determine if groups differ on more

than one dependent variable Takes two scores on each dependent variable

and creates one score - vector- to representeach subject’s scores on all dependentvariables

Mean of the vector scores is a single scorecalled a centroid




Multivariate Analysis of Variance (MANOVA)

Spaces represented by the vectors differ

significantly

Used to conceptualize and analysis nature of

the interrelated characteristics and if they

differ




Nonparametric Test

Test of statistical significance that doesnot rely on any assumptions about theshape or variance of the populationscores

– Chi-square ( 2)

– Mann-Whitney U Test

– Wilcoxon Signed Rank Test




Chi-square ( 2)

Determines if data in form of

frequently counts are distributeddifferently from different samples

phi and contingency coefficients can be

computed to estimate magnitude of relationship of variables in a chi-square table




Mann-Whitney U Test

Determines whether distribution of scores

of two independent samples are significant If U is significant, bulk of scores in one

population

Used when it is assumed that homogeneityof sample variances underlying the t test isgrossly violated




Wilcoxon Signed Rank Test

Determines whether distribution of scores

of two samples differ significantly from oneanother when scores of samples arecorrelated

Analogous with the t test for correlatedmeans except no assumptions of shape of the distribution or homogeneity of variance



Causal-Comparative vs. Correlational

Neither experimental

Does not involve manipulation of a

treatment variableRelationships are studied in both

Correlational-focus on magnitude and

direction of relationship

Causal-Comparative-focus ondifference between two groups

Descriptive Causal

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