Tools and Methods of Research Chapter 2

1. Tools and Methods of ResearchChapter 2

2. Introduction • Tool: A specific mechanism or strategy the researcher uses • Method: is the general approach (how to) that is taken to carry out research

3. Measurement • Strive for Objectivity • Don’t be influenced by your biases

4. “Nothing Exists that the Researcher Cannot Measure” (Some are just more defined) Two Types of Measurement: a) Substantial b) Insubstantial

5. Substantial • Substantial measurements are things being measured that have an obvious basis in the physical world. • Using Quantities: (a number and a unit) • The table is 15 inches long • Unbiased

6. Abstract data that exist only as concepts, ideas, opinions, or feelings. Example: asking someone for their opinion of something by asking them their feelingson the subject. Very Subjective and biased Insubstantial

7. Example • Question: How is President Obama doing so far in his administration? • Insubstantial answers: opinionated phrases. • Substantial answer: rating on a scale of 1 to 10. Assign a number to a phrase Ex: • 1- one of America’s worst President’s • 10- one of America’s greatest Presidents

8. Measurement Defined (pg. 24) • “Limiting the data of any phenomenom-substantial or insubstantial-so that those data may be interpreted and ultimately compared to an acceptable qualitative or quantitative standard”

9. Data Analysis- Measurement • Measurement is ultimately a comparison. • Any form of measurement falls into one of four categories.

10. 4 Scales of Measurement • 1. Nominal • 2. Ordinal • 3. Interval • 4. Ratio

11. Nominal Scale • You assign names to data in order to measure it • Example • Measuring a group of children • Divide into 2 groups: Girls and Boys • Each subgroup is thereby measured by a girl’s name or a boy’s name • Only a few statistics are appropriate for analyzing this kind of data: (frequencies, modes, % …Chi square)

12. Ordinal Scale • Measurements are relative • Type of statistics used expands beyond nominal Examples: Median, percentile rank; Spearman’ rank of Correlation

13. Ordinal Scale • Compare pieces of data in terms of being greater > or less < than the others. • Example • Grades of proficiency • Skilled • Unskilled • Overskilled

14. Interval Scale • Uses equal units of measurement • Its zero point is established arbitrarily • Example • Measuring temperature using Fahrenheit • Intervals between degrees reflect equal changes in temperature • The zero point is not a total absence of heat • Example: O degress Fahrenheit does not indicate absence of heat

15. Validity and Reliability of Measurement

16. Validity • Validity is whether or not a tool of measurement has the ability to properly measure what it is suppose to measure. • Example: A test may be intended to measure a certain characteristic, and it may be called a measure of that characteristic, but these things don’t necessarily mean that the test actually measures what its authors say it does. • Example” Does an IQ test accurately measure all types of IQ’s? (academic IQ, social IQ, mechanical IQ, etc…

17. Reliability • When the conditions for measurement are consistent for each measurement. • Instruments used to measure insubstantial data are less reliable than substantial • Ex: On a teacher Availability scale a student rates the same teacher a score of 60 one day when the teacher is less available and 95 a different day when the teacher is more available

18. Conclusion • Both reliability and validity reflect the degree to which we may have error in our measurements. • Validity errors are usually due to the instrument itself, and reliability errors are usually due to the use of the instrument.