CAS 301 - Week 4 Notes
CAS 301 - Week 4 Notes CAS 301
Cal State Fullerton
Popular in Inquiry & Methodology in Child Development
Popular in Child and Adolescent Studies
This 5 page Class Notes was uploaded by Caru on Wednesday September 14, 2016. The Class Notes belongs to CAS 301 at California State University - Fullerton taught by Sarana Roberts in Fall 2016. Since its upload, it has received 3 views. For similar materials see Inquiry & Methodology in Child Development in Child and Adolescent Studies at California State University - Fullerton.
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Date Created: 09/14/16
CAS 301 Week 4 Chapter 5: Measurement Concepts ● Reliability of Measures ○ Reliability the consistency or stability of a measure of behavior ■ Something reliable would continually yield the same result ■ Something is unreliable if it yields varied results ○ True score the real score on the variable ○ Measurement error the degree to which a measurement deviates from the true score value ■ High measurement error = low validity ○ In research, you cannot continually test a subject 50 or 100 times; therefore, it is important to use a reliable measure ■ This single measure should reflect the person’s true score ○ Reliability is likely to be achieved when researchers use careful measurement procedures ■ Ex. carefully training observers to record behavior ■ Ex. paying close attention to the way questions are phrased ■ Ex. recording electrodes are placed on the body to measure physiological reactions ■ Reliability can often be increased by making multiple measures ● Most commonly seen when assessing personality traits and cognitive abilities ○ We cannot directly observe the true score and error components of an actual score on the measure ■ We can assess the statistical stability of measures using correlation coefficients(a number that tells us how strongly two variables are related to each other) ■ Pearson productmoment correlation coefficient most common correlation coefficient when discussing reliability; symbolized as r ● Can range from 0.00 to +1.00 and 0.00 to 1.00 ● A correlation of 0.00 tells us that the two variables are not related at all ● The closer a correlation is to 1.00(whether positive or negative), the stronger the relationship ● The positive and negative signs provide information about the relationship ○ Positive correlation coefficient = positive linear relationship(/) ○ Negative correlation coefficient = negative linear relationship(\) ● To assess the reliability of a measure, we will need to obtain at least two scores on the measure from many individuals > if the measure is reliable, the two scores should be very similar and the Pearson correlation coefficient should be a high positive correlation ○ TestRetest Reliability ■ Testretest reliability assessed by measuring the same individuals at two points in time ● Ex. the reliability of a test of intelligence could be assessed by giving the measure to a group of people on one day and again a week later ○ We would have two scores for each person, and a correlation coefficient could be calculated to determine the relationship between first test score and the retest score ● For most measures, the reliability coefficient should be at least .80 for it to be accepted as reliable ● Problem: b ecause this requires the same test to be administered twice to an individual, the correlation might be artificially high because the individuals remember how they responded the first time ■ Alternate forms reliability requires administering two different forms of the same test to the same individuals at two points in time ● Drawback: creating a second equivalent measure may require considerable time and effort ○ Internal Consistency Reliability ■ Internal consistency reliability the assessment of reliability using responses at only one point in time ● All items measure the same variable, so they should yield similar or consistent results ■ Splithalf reliability the correlation of the total score on one half of the test with the total score on the other half ● The two halves are created by randomly dividing the items into two parts ● Final measure will include items from both halves = more items = more reliable than either half by itself ○ This fact must be taken into account when calculating the reliability coefficie SpearmanBrown splithalf reliability coefficient) ● Downside: based on only one of many possible ways of dividing the measure into halves ■ Cronbach’s alpha provides us with the average of all possible splithalf reliability coefficients ● Scores on each item are correlated with scores on every other item ● Based on the average of all the interitem correlation coefficients and the number of items in the measure ● More items = higher reliability ■ Itemtotal correlations very informative; provide information about each individual item ● Examines the correlation of each item score with the total based on all items ● Items that do not correlate with the total score on the measure are actually measuring a different variable > can be eliminated to increase internal consistency reliability ○ Interrater Reliability ■ To make a rating or judgment, a rater uses instructions for making judgements ■ The single observations of one rater might be unreliable ■ Interrater reliability the extent to which raters agree in their observations ■ Cohen’s kappa commonly used indicator of interrater reliability ○ Reliability and Accuracy of Measures ■ Reliability does not tell us about whether we have a good measure of the variable of interest ■ The difference between the reliability and accuracy of measurements leave us the consideration of validity of measures ● Construct Validity of Measures ○ Construct validity concerns whether our methods of studying variables are accurate; adequacy of the operational definition of variables; in terms of measurement: a question of whether the measure that is employed actually measures the construct it is intended to measure ○ Indicators of Construct Validity ■ Face Validity ● Face validity the degree to which something measures what it is supposed to measure ● Not very sophisticated only involves a judgment of whether, given the theoretical definition of the variable, the content of the measure appears to actually measure the variable ● Not sufficient to conclude that a measure is valid ■ Content Validity ● Content validity based on comparing the content of the measure with the universe of content that define the constructs ■ Predictive Validity ● Predictive validity research that uses a measure to predict some future behavior ● Criterion is based on future behavior or outcomes ● Important when studying measures that are designed to improve our ability to make predictions ■ Concurrent Validity ● Concurrent validity demonstrated by research that examines the relationship between the measure and a criterion behavior at the same time (concurrently) ● Can take on many forms ● A common method is to study whether two or more groups of people differ on the measure in expected ways ● Another approach is to study how people who score either low or high on the measure behave in different situations ■ Convergent Validity ● Convergent validity the extent to which scores on the measure in question are related to scores on other measures of the same construct or similar constructs ● Measures of similar constructs should converge ■ Discriminant Validity ● Discriminant validity demonstrated when the measure is not related to variables when it should be related ● Reactivity of Measures ○ Reactivity awareness of being measured changes an individual’s behavior ○ A reactive measure tells us what the person is like when he or she is aware of being observed does not tell how a person would behave under natural circumstances ○ Measures of behavior vary in terms of their potential reactivity ○ There are ways to minimize reactivity ■ Ex. allowing time for individuals to become used to the presence of the observer or recording equipment ● Variables and Measurement Scales ○ Nominal Scales ■ Nominal scales categories with no numeric scales ● Ex. males and females ■ You can assign numbers, but they would be meaningless, except for identification ■ The independent variables is often a nominal/categorical variable ○ Ordinal Scales ■ Ordinal scales allow us to rank order the levels of the variable being studied ■ Categories can be ordered from first to last ● Ex. letter grades > categories that can be organized in a meaningful way A B C D F ■ Not a nominal scale ○ Interval and Ratio Scales ■ Interval scale the difference between the numbers on the scale is meaningful; numeric properties are literal ● The intervals between the numbers are equal in size ● Ex. thermometer measures temperature on an interval scale ● The zero on any interval scale is only an arbitrary reference point > there is no absolute zero ■ Ratio scales do have an absolute zero point that indicates the absence of the variable being measured ● Used when variables involve physical measures being studied ● Ex. length, weight, time
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