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CORRELATION
DESIGNS
HESTI APRILIA S. (20181111044)
ASTRI DEWI F. (20181111046)
WATCHARAKORN MAHAMAD (20181111069)
WHAT ARE CORRELATION DESIGNS
• Correlational designs provide an opportunity for you to predict scores and explain the relationship among variables.
In correlational research designs, investigators use the cor- relation statistical test to describe and measure the
degree of association (or relationship) between two or more variables or sets of scores. In this design, the
researchers do not attempt to control or manipulate the variables as in an experiment; instead, they relate, using the
correlation statistic, two or more scores for each person (e.g., a student motiva- tion and a student achievement
score for each individual).
• A correlation is a statistical test to determine the tendency or pattern for two (or more) variables or two sets of data to
vary consistently. In the case of only two variables, this means that two variables share common variance, or they co-
vary together. To say that two variables co-vary has a somewhat complicated mathematical basis. Co-vary means
that we can predict a score on one variable with knowledge about the individual’s score on another variable. A simple
example might illustrate this point. Assume that scores on a math quiz for fourth-grade students range from 30 to 90.
We are interested in whether scores on an in-class exercise in math (one variable) can predict the student’s math
quiz scores (another variable). If the scores on the exercise do not explain the scores on the math quiz, then we
cannot predict anyone’s score except to say that it might range from 30 to 90. If the exercise could explain the
variance in all of the math quiz scores, then we could predict the math scores perfectly. This situation is seldom
achieved; instead, we might find that 40% of the variance in math quiz scores is explained by scores on the exercise.
This narrows our prediction on math quiz scores from 30 to 90 to something less, such as 40 to 60.
WHAT ARE THE TYPES OF CORRELATIONAL
DESIGNS?
Essentially, there are 3 types of correlational research which are positive correlational
research, negative correlational research, and no correlational research. Each of these
types is defined by peculiar characteristics:
1.Positive Correlational Research
2.Negative Correlational Research
3.Zero Correlational Research
WHY WE HAVE TO USE CORRELATION
RESEARCH?
• You use this design when you seek to relate two or more variables to see if they influ- ence each
other, such as the relationship between teachers who endorse developmentally appropriate
practices and their use of the whole-language approach to reading instruc- tion (Ketner, Smith, &
Parnell, 1997). This design allows you to predict an outcome, such as the prediction that ability,
quality of schooling, student motivation, and academic coursework influence student achievement
(Anderson & Keith, 1997). You also use this design when you know and can apply statistical
knowledge based on calculating the cor- relation statistical test.
• Stanovich (2007) points out the following:
“First, many scientific hypotheses are stated in terms of correlation or lack of correlation, so that such
studies are directly relevant to these hypotheses…”
“Second, although correlation does not imply causation, causation does imply correlation. That is,
although a correlational study cannot definitely prove a causal hypothesis, it may rule one out.
“Third, correlational studies are more useful than they may seem, because some of the recently
developed complex correlational designs allow for some very limited causal inferences.
…some variables simply cannot be manipulated for ethical reasons (for instance, human
malnutrition or physical disabilities). Other variables, such as birth order, sex, and age are
inherently correlational because they cannot be manipulated, and, therefore, the scientific
knowledge concerning them must be based on correlation evidence.”
Once correlation is known it can be used to make predictions. When we know a score on one
measure we can make a more accurate prediction of another measure that is highly related to
it. The stronger the relationship between/among variables the more accurate the prediction.
When practical, evidence from correlation studies can lead to testing that evidence under
controlled experimental conditions.
THE STEPS IN CONDUCTING A
CORRELATIONAL STUDY :
• Step 1. Determine If a Correlational Study Best Addresses the Research Problem
• Step 2. Identify Individuals to Study
• Step 3. Identify Two or More Measures for Each Individual in the Study
• Step 4. Collect Data and Monitor Potential Threats
• Step 5. Analyze the Data and Represent the Results
• Step 6. Interpret the Results
WHAT ARE THE DATA COLLECTION METHODS
IN CORRELATIONAL RESEARCH?
Data collection methods in correlational research are the research methodologies
adopted by persons carrying out correlational research in order to determine the
linear statistical relationship between 2 variables. These data collection methods
are used to gather information in correlational research.
The 3 methods of data collection in correlational research are naturalistic
observation method, archival data method, and the survey method. All of these
would be clearly explained in the subsequent paragraphs.
1.Naturalistic Observation
Naturalistic observation is a
correlational research
methodology that involves
observing people's
behaviors as shown in the
natural environment where
they exist, over a period of
time.
The major advantages of
the naturalistic observation
method are that it allows the
researcher to fully observe
the subjects (variables) in
their natural state. However,
it is a very expensive and
time-consuming process
plus the subjects can
become aware of this act at
any time and may act
contrary.
2.Archival Data
Archival data is a type of correlational
research method that involves making
use of already gathered information
about the variables in correlational
research. Since this method involves
using data that is already gathered and
analyzed, it is usually straight to the
point.
For this method of correlational
research, the research makes use of
earlier studies conducted by other
researchers or the historical records of
the variables being analyzed. This
method helps a researcher to track
already determined statistical patterns
of the variables or subjects.
3.Survey Method
The survey method is the most
common method of correlational
research; especially in fields like
psychology. It involves random
sampling of the variables or the
subjects in the research in which the
participants fill a questionnaire
centered on the subjects of interest.
This method is very flexible as
researchers can gather large
amounts of data in very little time.
However, it is subject to survey
response bias and can also be
affected by biased survey questions
or under-representation of survey
respondents or participants.
These would be properly explained
under data collection methods in
correlational research.
WHAT ARE THE ADVANTAGES OF
CORRELATIONAL RESEARCH?
• In cases where carrying out experimental research is unethical, correlational
research can be used to determine the relationship between 2 variables. For
example, when studying humans, carrying out an experiment can be seen as
unsafe or unethical; hence, choosing correlational research would be the best
option.
• Through correlational research, you can easily determine the statistical
relationship between 2 variables.
• Carrying out correlational research is less time-consuming and less expensive
than experimental research. This becomes a strong advantage when working with
a minimum of researchers and funding or when keeping the number of variables
in a study very low.
• Correlational research allows the researcher to carry out shallow data gathering
using different methods such as a short survey. A short survey does not require
the researcher to personally administer it so this allows the researcher to work
WHAT ARE THE DISADVANTAGES OF
CORRELATIONAL RESEARCH?
• Correlational research is limiting in nature as it can only be used to determine the statistical
relationship between 2 variables. It cannot be used to establish a relationship between more
than 2 variables. It does not account for cause and effect between 2 variables as it doesn't
highlight which of the 2 variables is responsible for the statistical pattern that is observed. For
example, finding that education correlates positively with vegetarianism doesn't explain whether
being educated leads to becoming a vegetarian or whether vegetarianism leads to more
education.Reasons for either can be assumed, but until more research is done, causation can't
be determined. Also, a third, unknown variable might be causing both. For instance, living in the
state of Detroit can lead to both education and vegetarianism.Correlational research depends
on past statistical patterns to determine the relationship between variables. As such, its data
cannot be fully depended on for further research. The information received from correlational
research is limited. Correlational research only shows the relationship between variables and
does not equate to causation.
CONCLUSION
• Findings from correlational research can be used to determine prevalence and
relationships among variables, and to forecast events from current data and
knowledge. In spite of its many uses, prudence is required when using the
methodology and analysing data.
REFERENCES :
• Creswell, J. W. (2012). Educational research Planning, conducting, and
evaluating quantitative and qualitative research (4th ed.). Boston, MA Pearson
• https://psychcentral.com/blog/the-importance-of-correlational-studies#1
THANKYOU

More Related Content

Correlation

  • 1. CORRELATION DESIGNS HESTI APRILIA S. (20181111044) ASTRI DEWI F. (20181111046) WATCHARAKORN MAHAMAD (20181111069)
  • 2. WHAT ARE CORRELATION DESIGNS • Correlational designs provide an opportunity for you to predict scores and explain the relationship among variables. In correlational research designs, investigators use the cor- relation statistical test to describe and measure the degree of association (or relationship) between two or more variables or sets of scores. In this design, the researchers do not attempt to control or manipulate the variables as in an experiment; instead, they relate, using the correlation statistic, two or more scores for each person (e.g., a student motiva- tion and a student achievement score for each individual). • A correlation is a statistical test to determine the tendency or pattern for two (or more) variables or two sets of data to vary consistently. In the case of only two variables, this means that two variables share common variance, or they co- vary together. To say that two variables co-vary has a somewhat complicated mathematical basis. Co-vary means that we can predict a score on one variable with knowledge about the individual’s score on another variable. A simple example might illustrate this point. Assume that scores on a math quiz for fourth-grade students range from 30 to 90. We are interested in whether scores on an in-class exercise in math (one variable) can predict the student’s math quiz scores (another variable). If the scores on the exercise do not explain the scores on the math quiz, then we cannot predict anyone’s score except to say that it might range from 30 to 90. If the exercise could explain the variance in all of the math quiz scores, then we could predict the math scores perfectly. This situation is seldom achieved; instead, we might find that 40% of the variance in math quiz scores is explained by scores on the exercise. This narrows our prediction on math quiz scores from 30 to 90 to something less, such as 40 to 60.
  • 3. WHAT ARE THE TYPES OF CORRELATIONAL DESIGNS? Essentially, there are 3 types of correlational research which are positive correlational research, negative correlational research, and no correlational research. Each of these types is defined by peculiar characteristics: 1.Positive Correlational Research 2.Negative Correlational Research 3.Zero Correlational Research
  • 4. WHY WE HAVE TO USE CORRELATION RESEARCH? • You use this design when you seek to relate two or more variables to see if they influ- ence each other, such as the relationship between teachers who endorse developmentally appropriate practices and their use of the whole-language approach to reading instruc- tion (Ketner, Smith, & Parnell, 1997). This design allows you to predict an outcome, such as the prediction that ability, quality of schooling, student motivation, and academic coursework influence student achievement (Anderson & Keith, 1997). You also use this design when you know and can apply statistical knowledge based on calculating the cor- relation statistical test. • Stanovich (2007) points out the following: “First, many scientific hypotheses are stated in terms of correlation or lack of correlation, so that such studies are directly relevant to these hypotheses…” “Second, although correlation does not imply causation, causation does imply correlation. That is, although a correlational study cannot definitely prove a causal hypothesis, it may rule one out. “Third, correlational studies are more useful than they may seem, because some of the recently developed complex correlational designs allow for some very limited causal inferences.
  • 5. …some variables simply cannot be manipulated for ethical reasons (for instance, human malnutrition or physical disabilities). Other variables, such as birth order, sex, and age are inherently correlational because they cannot be manipulated, and, therefore, the scientific knowledge concerning them must be based on correlation evidence.” Once correlation is known it can be used to make predictions. When we know a score on one measure we can make a more accurate prediction of another measure that is highly related to it. The stronger the relationship between/among variables the more accurate the prediction. When practical, evidence from correlation studies can lead to testing that evidence under controlled experimental conditions.
  • 6. THE STEPS IN CONDUCTING A CORRELATIONAL STUDY : • Step 1. Determine If a Correlational Study Best Addresses the Research Problem • Step 2. Identify Individuals to Study • Step 3. Identify Two or More Measures for Each Individual in the Study • Step 4. Collect Data and Monitor Potential Threats • Step 5. Analyze the Data and Represent the Results • Step 6. Interpret the Results
  • 7. WHAT ARE THE DATA COLLECTION METHODS IN CORRELATIONAL RESEARCH? Data collection methods in correlational research are the research methodologies adopted by persons carrying out correlational research in order to determine the linear statistical relationship between 2 variables. These data collection methods are used to gather information in correlational research. The 3 methods of data collection in correlational research are naturalistic observation method, archival data method, and the survey method. All of these would be clearly explained in the subsequent paragraphs.
  • 8. 1.Naturalistic Observation Naturalistic observation is a correlational research methodology that involves observing people's behaviors as shown in the natural environment where they exist, over a period of time. The major advantages of the naturalistic observation method are that it allows the researcher to fully observe the subjects (variables) in their natural state. However, it is a very expensive and time-consuming process plus the subjects can become aware of this act at any time and may act contrary. 2.Archival Data Archival data is a type of correlational research method that involves making use of already gathered information about the variables in correlational research. Since this method involves using data that is already gathered and analyzed, it is usually straight to the point. For this method of correlational research, the research makes use of earlier studies conducted by other researchers or the historical records of the variables being analyzed. This method helps a researcher to track already determined statistical patterns of the variables or subjects. 3.Survey Method The survey method is the most common method of correlational research; especially in fields like psychology. It involves random sampling of the variables or the subjects in the research in which the participants fill a questionnaire centered on the subjects of interest. This method is very flexible as researchers can gather large amounts of data in very little time. However, it is subject to survey response bias and can also be affected by biased survey questions or under-representation of survey respondents or participants. These would be properly explained under data collection methods in correlational research.
  • 9. WHAT ARE THE ADVANTAGES OF CORRELATIONAL RESEARCH? • In cases where carrying out experimental research is unethical, correlational research can be used to determine the relationship between 2 variables. For example, when studying humans, carrying out an experiment can be seen as unsafe or unethical; hence, choosing correlational research would be the best option. • Through correlational research, you can easily determine the statistical relationship between 2 variables. • Carrying out correlational research is less time-consuming and less expensive than experimental research. This becomes a strong advantage when working with a minimum of researchers and funding or when keeping the number of variables in a study very low. • Correlational research allows the researcher to carry out shallow data gathering using different methods such as a short survey. A short survey does not require the researcher to personally administer it so this allows the researcher to work
  • 10. WHAT ARE THE DISADVANTAGES OF CORRELATIONAL RESEARCH? • Correlational research is limiting in nature as it can only be used to determine the statistical relationship between 2 variables. It cannot be used to establish a relationship between more than 2 variables. It does not account for cause and effect between 2 variables as it doesn't highlight which of the 2 variables is responsible for the statistical pattern that is observed. For example, finding that education correlates positively with vegetarianism doesn't explain whether being educated leads to becoming a vegetarian or whether vegetarianism leads to more education.Reasons for either can be assumed, but until more research is done, causation can't be determined. Also, a third, unknown variable might be causing both. For instance, living in the state of Detroit can lead to both education and vegetarianism.Correlational research depends on past statistical patterns to determine the relationship between variables. As such, its data cannot be fully depended on for further research. The information received from correlational research is limited. Correlational research only shows the relationship between variables and does not equate to causation.
  • 11. CONCLUSION • Findings from correlational research can be used to determine prevalence and relationships among variables, and to forecast events from current data and knowledge. In spite of its many uses, prudence is required when using the methodology and analysing data.
  • 12. REFERENCES : • Creswell, J. W. (2012). Educational research Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Boston, MA Pearson • https://psychcentral.com/blog/the-importance-of-correlational-studies#1