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Instructional Measurement
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Research Methods I
Kinds of Data and
Levels of Measurement
Review The Scientific Method – Conducting a
Study1. Understanding Nature of Problem
1. Literature Review2. Research Question - Hypothesis
2. Test Hypothesis empirically1. Deciding on measurements2. Data collection3. Data analysis
3. Interpret results and draw conclusions
Variables and MeasurementsVariables:Characteristics that can take on different values for
different members of a group Independent variables Dependent variables
Construct:Hypothetical concepts that describe and explain
behavior (e.g. self-esteem) operational definition of construct
Measurements:“Assignment of numbers to aspects of objects,
persons or events.”
Kinds of DataLevels of Measurement
Qualitative / Discrete Data Separate, indivisible categories (e.g. male, female)
Nominal (categorical) (Ordinal)
Quantitative / Continuous Data Infinite number of possible values that fall between
two observed values Interval Ratio
Nominal Level of Measurement
Data in a set of categories that have different names It is arbitrary; no logical orderingHas to do with names
E.g. gender, race, religion, kind of profession
N-category nominal scales Dichotomies (gender) Five category (ethnicity: African-American,
Caucasian, Asian, Native American, Hispanic)
Ordinal Level of Measurement
Ranked in terms of magnitude Distances between variables or exact
amount of variables does not have to be known
In papers grouped ordinal data is often used (how many people are in each category
Ordinal Level of Measurement – Likert Scales
Item pool concerning referent in question Level of agreement to each statement Average responses to get final score Logical sequence (order) May be treated as continuous variables in
analyses even though they are actually ordinal
Interval Level of Measurement
Ordered categories that are all intervals of exactly the same size
For interval data zero is an arbitrary pointDoes not mean the absence of measured
characteristic Arithmetic operations can be performed with
interval dataThere are some limitations
Ratio Level of Measurement
Ratio data is like interval data, except the origin of the scale represents the absence of the characteristic measured
Examples of ratio level measurements are
Is our measure valid?
Definition:
Validity describes how well as measure actually assesses what you want it to
Decide how to measure variables Describes soundness and appropriateness
of a measure for purpose of study
Validity Content validity: Does measure cover all
different domains of the concept?Face validity: How is measure viewed by others
as covering the concept?Sampling-content validity: everything covered?
Criterion validity: How well do measures of convenience assess criterion of interest
Construct validity: Does the measure assess underlying theoretical construct?
Reliability To which extent do two sets of measurements of
the same characteristic on the same people duplicate each other
A reliable measure is free of measurement errorTest-retest reliability (same people, different time)
Inter-rater agreement (same people, same time)
Internal-consistency (consistency of answers across items)
Problem with measurement error and reliability - variability