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40 Cards in this Set
- Front
- Back
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Validity
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-refers to whether or not the instrument accurately measures what it is suppose to measure
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Content Validity
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-represents the universe content or domain (population) of a given construct
-subtype is face validity: basically verifies that the instrument gives the appearance of measuring the concept |
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Criterion-related Validity
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-indicates to what degree the subject’s performance on the measurement tool and the subject’s actual behavior are related
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Concurrent:
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the degree of correlation of two measures of the same concept being measured at the same time.
Ex. SAT scores compared to the high school record |
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Predictive:
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the degree of correlation of the concept and some future measure of the same concept
Ex. SAT scores predict how well one will do in college |
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Construct Validity
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-based on the extent to which a test measures a theoretical construct or trait
-establishment of construct validity is a complex process often involving several studies and approaches that include |
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• Hypothesis Testing Approach
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investigator uses the theory or concept underlying the instrument to validate the instrument
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• Convergent validity
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refers to a search for other measures of the construct
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• Divergent validity
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uses measurement approaches that differentiates one construct from another that may be similar or opposite of each other
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• Multitrait-multimethod
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combines both convergent and divergent
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• Contrasted-Groups Approach
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the researcher identifies 2 groups of individuals who are expected to score very high or extremely low in the characteristics being measured by the instrument
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Who rose as superpowers after WWII?
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US and USSR
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Threats to Internal Validity (did the independent variable cause the change in the dependent variable)
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1. History
• what was going on during the time of the study • Sometimes environment has an affect on the outcomes of the study 2. Maturation • The participants mature and behavior changes with maturity 3. Testing • Person may recognize things that were on the pretest that are the same as the posttest 4. Instrumentation • Was the instruments calibrated right? If not can influence the study’s outcomes 5. Mortality • Lost of participants 6. Selection Bias • Pool of individuals may be similar to each other without knowing it |
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Reliability
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Refers to the ability of the instrument to produce the same result on different occasions
Includes: • Stability of an instrument: where the instrument is able to produce the same results with repeated measures • Homogeneity of an instrument: where the instrument is measuring all the same concepts or characteristics • Equivalence is the tool produces the same results when equivalent or parallel procedures are used -use this to make sure we are reducing error |
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• Reliability Coefficient Interpretation
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expresses the relationship among the error variance, true variance and the observed score and ranges from 0-1 (0.07 is acceptable in research studies)
Test used to calculate reliable coefficient (Way to assure results are because of the intervention and not by chance) |
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Test-retest
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administration of the same instrument to same subjects under similar conditions on 2 or more occasions
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Parallel or alternate form
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same individuals tested within a specific interval but a different form of same test is given to the subjects on 2nd testing
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Split haf
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involves dividing the scale in half and making comparisons
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Kuder-Richardson
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used for instruments that have a dichotomous response format-yields correlation based on consistency of responses to all items of a single form of a test that is administered one time
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Interrater reliability:
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used in direct measurements of observed behavior
-2 or more individuals make observations on several occasions and compare findings -expressed as a percentage of agreement between the observers or as a correlation coefficient of the scores assigned to the observed behaviors |
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Levels of Measurement 1. Nominal
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lowest of the four measurement categories
-used when the data can be organized into categories of a defined property but the categories cannot be compared -categories must not be orderable Ex. Gender, ethnicity, martial status and diagnosis |
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Levels of Measurement 2. Ordinal
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-data are assigned to categories that can be ranked
-one category is judged to be or ranked higher than another -considered to have unequal intervals |
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Levels of Measurement3. Interval Scale
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-uses interval scales which have equal numerical distances between the intervals
-represents a continuum of values -magnitude of attribute can be more precisely defined -lacks a zero point Ex. Temperature is most common interval scale |
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Levels of Measurement4. Ratio Scale
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highest form of measurement and meets all of the other forms of measurement: mutually exclusive, exhaustive, ordered ranks, equally spaced intervals and continuum values
-have absolute zero points and means absence of the property Ex. Weight, length, volume |
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Descriptive Statistics
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• Used in any study where the data uses numbers
• Also known as summary statistics • Summary statistics and visual displays that illustrate the characteristics of a study sample • Includes measures of central tendency • Can also be used to describe differences between groups or variables |
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• Frequency
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involves ordering the numbers from lowest to highest and counting the # of times each value appears in the data
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Inferential Statistics
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goes beyond describing the sample
-allows the researcher to generalize from the sample to the larger population -based on the assumption that the sample is randomly selected Sample vs. Population Sample: subset of the population Population: is the entire set of elements that the researcher wants to study |
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Statistical Significance
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-refers to that there is a relationship between the variables and that the results were not because of chance
-just because there is statistical significance does not mean there is clinical significance |
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Clinical Significance
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-refers to does the statistical significance has relevance to practice
-is the findings big enough to change the practice |
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Nonsignificance
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-refers to that any observed difference or relationships could have been the result of chance fluctuations
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Ways of Testing Statistical Significance (causality)
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Causality is a way of knowing that one thing causes the other
-They can be used to understand the effect of an intervention and are critical to the development of nursing science -These statistic examine causality by examining differences between and among groups |
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• T-test for independent Samples
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Used when the researcher is interested in comparing 2 independent groups of subjects with respect to the average values on a dependent variable
-Interval level data is important for this test -When the t-test is used a t- value is computed and compared to the critical value in the table and if the t-value is greater than or equal to the value in the table the groups are significantly difference -If the data is nominal or ordinal a chi-square test can be used: which examines is the variables related or independent from one another |
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• Paired T-test
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-Used when the researcher wants to compare 2 sets of mean scores from the same subjects
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• Analysis of Variance
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procedure used to test significant differences between averages
-use of 3 or more mean scores -reported as a F-statitistic and if the computed F-value is greater than or equal to the F-value in statistical table then there is a significant difference between the groups Ex. The researcher is interested and comparing 3 interventions to help people stop smoking |
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Probability
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-requires that every element in a population have an equal chance of being selected
-helps to have a representative sample |
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• Simple Random Sampling
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-each element of the population have an equal chance of inclusion into the sample
-assign each subject a number and close eyes and point to numbers randomly until sample size is reaches |
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• Stratified Sample
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-population is divided into strata
-sample a number of people from each strata |
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• Systematic Sampling
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-involves drawing from every nth element in a population
Ex. Drawing every 10th element in a population |
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• Cluster Sampling
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-requires that the population be divided into clusters or groups
-most large scale studies use this |
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Nonprobability
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-there is no way of knowing that the probability of each element will be included in the sample
-results are NOT generalized to the population |
