Correlation Al Research

8/13/2019 Correlation Al Research

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Correlational Research

Types of Correlational Research Designs

The first type of correlational design, explanatory design, is conducted when researchers want to

explore the extents to which two or more variables co-vary, that is, where changes in onevariable are reflected in changes in the other (Creswell, 2!, p" #$!%" &hen conducting an

explanatory correlational study, researchers typically collect data at one time as their focus is not

based on future or past performance of participants" Thus, when analy'ing the findings ofexplanatory correlation research, researchers analy'e participants as a single group rather than

creating subcategories of participants" inally, in this type of study researchers collect two scores

from each participant as each score represents each variable being studied (Creswell, 2!%"

The second type of correlational design,prediction design, is used by researchers when the

purpose of the study is to predict certain outcomes in one variable from another variable that

serves as the predictor" )rediction designs involve two types of variables* apredictor variable

and a criterion variable" &hile the predictor variable is utili'ed to ma+e a forecast or prediction,the criterion variable is the anticipated outcome that is being predicted" )rediction studies can

usually be identified rather easily by research consumers simply by ta+ing note of the title of apublished study as most published prediction studies include the word prediction in the

articles title" The time at which variables are measured also differs in prediction studies as the

predictor variable is typically measured at one time while the criterion variable is usuallymeasured at a later date" )rediction studies also include a forecast of anticipated future

performance, as well as advanced statistical procedures including multiple regression" or

further information about multiple regressions see (lin+ to statistics portion of site% (Creswell,

2!%"

Characteristics of Correlational Research

ny time a researcher has at least two scores, a graph called ascatterplot can be used to provide

a visual representation of the data that has been collected" .ach point on a scatterplot represents

two scores provided by one person" /esearchers must select the scores for one variable to beplotted on the x-axis (the hori'ontal axis of the graph% while scores for the second variable are

plotted on the y-axis (the vertical axis of the graph%" 0catterplots are vitally important to

correlational research as they allow researchers, as well as research consumers, to determine the

following by loo+ing at patterns within the entire group of data points (Creswell, 2!1 odico etal", 23%*

The form of the relationship

The type of association

The existence of extreme scores

The direction of the relationship

The degree of the relationship

Consider the following situation*


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4r" Thomas has noticed that it seems as though students who earn higher scores on their

homewor+ assignments typically also score higher on the 5owa ssessments" 4r" Thomas

wonders if there is a relationship between the amount of time that the students spend onhomewor+ each night and their 5owa ssessment scores" Thus, 4r" Thomas as+s his six grade

students to report the amount of time (in minutes% that they spend each evening completing

homewor+" 4r" Thomas then created the following table with each students name, 5owassessment 6ational 0tandard 0core and the amount of time each student reported spending on

homewor+ each night"

Student

Iowa

Assessment

National

Standard Score

Average Time

Spent on

Homework

Nightly

4atthew 782 9ane 73: 7

;aniel 7# 7

9ose 7! 7rmando 7$ #

rittney 273 8$


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4r" Thomas is satisfied to see that there is a relationship between the amount of time his students

reported spending on homewor+ and their 5owa ssessment 6ational 0tandard 0cores" 5n fact,

4r" Thomas reali'es that a moderate, positive linear relationship exists between the variables he

studied" @ow does 4r" Thomas ma+e this determinationA irst, when reading a scatterplot anindividual should consider the form of an association between two variables illustrated on the

graph" ssociations between variables in correlational research can either be linear, nonlinear, or

uncorrelated" inear associations occur when the scores on one variable relate to the scores on

the second variable" or instance, 4r" Thomas +nows that the association between the amount oftime students spend on homewor+ and their 5owa ssessment scores is linear because, generally,

students 5owa ssessment scores relate to the amount of time they spent on homewor+" Thus,the data points on 4r" Thomas scatterplot form a single line (Creswell, 2!%"

inear relationships, such as 4r" Thomas, can be further subcategori'ed as either a positive

linear relationshipor a negative linear relationship" )ositive linear relationships (such as the one

depicted in 4r" Thomas scatterplot% occur when high scores in one variable relate to high scores

for the second variable or low scores in one variable relate to low scores for the second variable

being studied" 6egative linear relationships occur when high scores of one variable relate to lowscores in the second variable (Creswell, 2!%"

6onlinear relationships, or curvilinear relationships, are characteri'ed by a B-shape relationshipbetween variables" This means that the direction of the relationship between the variables differs

according to different levels of the variable (odico et al", 23%" curvilinear relationship

might occur if a researcher were to study the relationship between the amount of extracurricularactivities in which students participate and students grades because, while it is important for a

student to be involved in activities, being involved in too many activities as well as being


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involved in too few extra-curricular activities might be detrimental to a students grades" 0ee

igure 7 to view an example of the different types of relationships"

The third form of association that a scatterplot can display is an uncorrelated relationship" This

distribution occurs when two variables are not related to one another and are instead independent

of each other" Thus, an individuals score on one variable does not tell anything about what theother variables score might be (Creswell, 2!%"

igure 7

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0 2 4 0

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)ositive inear 6egative inear 6o Correlation Curvilinear Curvilinear

5f a relationship is linear, a correlation coefficientis useful as it describes and measures the

degree and strength of a relationship between two variables" Correlational coefficients can range

from -7" to 7" with positive numbers used to identify a positive relationship and negativenumbers being used to identify a negative relationship" 6o linear association is indicated with a

correlation coefficient of "" Thus, the closer a correlation coefficient is to ", the wea+er is

the relationship between variables (Creswell, 2!%" The following table can be used todetermine the strength of a relationship when a correlation coefficient is provided"

Correlation Coefficient Strength of Relationship

D":-7" 0trong

D"#-"3= 4oderateD"-"2= 6one ("% to wea+

or further information regarding correlation coefficients see (the 0tatisticsportion of site%"5fcorrelation coefficients are not provided for some reason, an individual can typically get an idea

of the strength of a linear relationship by loo+ing at the spread of data points in the scatterplot"

or instance, a stronger relationship typically exist when data points are grouped along in a morenarrow pattern1 whereas, a scatterplot in which data points are scattered inconsistently in the

graph is indicative of a wea+er relationship (odico, et al", 23%" 5n published research articles,

correlation coefficients are often displayed in a table called a correlation matrix(Creswell,

2!%. correlation matrix presents all of the possible correlations between the variables

included in a study" The following is an example of a correlation matrix*

!ample of Correlation "atri! # $ % & ' (

7" Time 0pent on @omewor+

7"
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2" 5T>0 600

"$!

E

7"

#" F)

"2=

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3

7"

8" 5nvolvement in .xtracurricular ctivities

"2

7

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$ "$$

7"

$" .mployment

"#

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"8

8

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3" 0ocioeconomic 0tatus -"33 -"#

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"27

7"

There are several things research consumers should note when attempting to glean information

from a correlation matrix" irst, each variable is displayed across the left-hand side of the matrix,as well as across the top of the matrix, usually included across the top represented by the number

to which they have been assigned" .ach variable has a correlation coefficient of 7" with itself"

Gn many correlation matrices the correlation coefficient for a variable and itself is often left out

of the table for this reason" @alf of correlation matrices are left empty as the empty part wouldinclude correlations identical to those presented in the bottom-half of the matrix" steris+s beside

correlation coefficients listed in a correlation matrix indicate that the correlation between two

variables isstatistically significant(lin+ to statistics explanation of statistical significance%(0lavin, 2:%"

Conducting a Correlational Study

correlational research design is useful to researchers who are interested in determining to what

degree two variables are related, however, correlational research does not prove arelationship1 rather, it indicates an association between two or more variables (Creswell, 2!%"

&hen a correlational research design is appropriate for a study, it can be designed by followingthe steps outlined by Creswell (2!% and odico et al" (23%*

Identify two variables that may be related- researchers often select variables to study witha correlational research design by reading published studies previously conducted by

researchers" Gther individuals tend to select variables from real-world situations as they

are interested in findings that are specific to their own situation" &hen selecting variables

for correlational studies, researchers should avoid the shotgun approach" The shotgunapproach occurs when researchers select every and any variable they can thin+ to include"

This approach is discouraged for correlational studies as the greater the amount of

relationships a researcher attempts to examine with correlational research, the more li+ely

the researcher is to determine that at least one relationship is statistically significantwhen, in fact, the significance was only due to chance" 5n fact, if a significance level of "

$ is used, then $ times out of 7, one may get a significant correlation due to chance(odico et al", 23, p" 2##%" This is especially problematic as there is no way for a

researcher to determine which correlations are due to chance and which represent a true

relationship between variables"


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Select a sample-0amples should be selected randomly and include at least # individuals

willing to parta+e in the study" Bsing a greater number of participants allows researchersto generali'e findings to the population of interest" /esearchers should also select a

heterogeneous sample that allow for a range of scores" Hariability in scores allows

researchers using correlational methods to determine whether patterns of data exist"

Correlational studies in which a homogenous sample is used and produces a limited rangeof scores is describes as having a restriction of range" &ide ranges of scores are

necessary as they allow researchers to determine the true relationship between variables"

Select a method of measurement-Gften the most complex part of a correlational study isdetermining how to effectively measure each variable" s with other research designs,

tools should be determined to be both valid and reliable" (lin+ to validity and reliability

portion of site% Bsually in correlational research two measurement tools are reIuired aseach tool measures one of the two variables involved in the study"

Collect necessary data-Correlational studies reIuire that researchers obtain data for each

variable from each participant" or instance, if an individual wanted to investigate therelationship between the length of an intervention and the success of an intervention, for

each student who participates in the study researchers would need to collect dataregarding both the length of the intervention implemented for the student and the success

of the intervention" Collecting data can be especially difficult for researchers conducting

prediction correlational studies as prediction studies reIuire data collection at more thanone point in time" 5n such cases, researchers often assign numbers to participants to

ensure that data remains confidential while the researcher is waiting to collect data for the

criterion variable"

Analyze the data-;ata from correlational research is analy'ed by using statistical tests

that depend greatly on the type of variables being studied" Hariables can be eithercontinuous,meaning that they change according to small increments (e"g" test scores%, or

dichotomous,in which the variable is divided into categories (e"g" gender, grade%"

Interpret results-&hen attempting to interpret results, researchers consider both the

strength and the si'e of the correlation coefficient" Gften researchers present findings ofcorrelational research in a correlational matrixin which each variable is numbered in a

table to show correlations between variables" steris+s are often used to indicate

correlations that arestatistically significant" 0tatistical significance for correlationalresearch indicates that the probability that the obtained correlation between two variables

is due to chance is less than "$ or "7 (depending on which cutoff a researcher has

selected%" lac+ of statistical significance between two variables means that anyrelationship between the variables could be due to chance" @owever, it is important to

note that statistical significance is li+ely to occur if a large sample is used by researchers

regardless of whether a true relationship exist between two variables" &hen ma+ing

predictions for individuals, a strong or very strong correlation is typically necessary"0tatistical significance indicates an association between two variables not causation"


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/eferences

Charles, C" J 4ertler, C" (22%"Introduction to Educational esearch" >oston* 4* llyn J

>acon"

Creswell, 9" (2!%"Educational research! "lanning, conducting, and evaluating

#uantitative and #ualitative research.6ew 9ersey* )earson* 4errill )rentice @all"

odico, 4", 0paulding, ;", J Hoegtle, ass"

0lavin, /" (2:%"Educational research in an age of accountability" >oston* )earson

.ducation"

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Correlation Al Research