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