Understanding Research Methods: Terms & Concepts in Quantitative & Qualitative Research, Quizzes of Statistics

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research question

a measurable inquiry that add or extends on a body of knowledge TERM 2

three types of research

questions

DEFINITION 2 qualitative, quantitative, and mixed methods TERM 3

qualitative research

questions

DEFINITION 3 in which you use qualitative research questions to answer the research question TERM 4

quantitative research

questions

DEFINITION 4 in which you use quantitative research procedures to answer the research question; this type of research is the main focus in Stats of Social Science TERM 5

mixed methods research

questions

DEFINITION 5 in which you use both quantitative and qualitative research procedures to answer the research question

phenomenon of interest

a specific measure related to the research question, being recorded where random outcomes occur per observation TERM 7

population

DEFINITION 7 subjects (living or nonliving) that the question is trying to inquire information about TERM 8

What issues arise that prevent a researcher

from answering the research question

directly?

DEFINITION 8 Costly, Time consuming, prone to experience bias TERM 9

nonresponse bias

DEFINITION 9 bias that occurs when the researcher is not able to capture a response due to a refusal or because the subject is unavailable TERM 10

response bias

DEFINITION 10 bias that occurs due to the result of falsification of answers by subjects or question confusion

statistics

the art and science of learning from data TERM 12

sample

DEFINITION 12 a subset of subjects from the population TERM 13

random variable

DEFINITION 13 an abbreviated description of the phenomenon of interest that stores the random outcomes; there are two types: discrete and continuous. the type of random variable you use determines what statistical test you will use later to find a solution TERM 14

discrete random variable

DEFINITION 14 a random variable is discrete if it has a finite or countable number of possible values; example: number of steps a jogger takes on his run TERM 15

continuous random variable

DEFINITION 15 a random variable is continuous if it has an infinite number of possible values where the only way to list them all is to have interval notation. example: height of a particular tree in a forest

levels of measurement

the format in which the random variable is collected TERM 17

nominal levels of measurement

DEFINITION 17 names or numbers only to represent separate categories. No name or value is more significant than another name or value. Possible values have no particular strength. Values only used to create classes. example: collecting names of people. TERM 18

ordinal levels of measurement

DEFINITION 18 names or numbers where only the comparison of "greater," "less," or "equal" are relevant. There is a rank in the names or values. Names or numbers where there is strength in the possible values. Values are still used to create classes. Example: ranking a professor's teaching ability via the teaching evaluations. TERM 19

interval levels of measurement

DEFINITION 19 considers relative order and the size of the difference between two measurements. Absolute zero does not exist. There is a strength in the possible values. You can talk about the difference between two possible values numerically, but only in terms of "greater than," "less than," or "more than." *** You can't say twice as much b/c it has no meaning or significant interpretation (you can't interpret the multiplicity). Example: temperatures in various cities. ABSOLUTE ZERO does not imply the absence of that unit, but instead zero is just a level in the list of possible values. (think 0 degrees is not equal to an absence of degrees) TERM 20

ratio levels of measurement

DEFINITION 20 considers relative order, size of the difference between two measurements, and the ratio of the two values. Absolute zero also exists. You can compare values in terms of their difference and by ratio. You can say "twice as much" or "three times as much." Absolute zero DOES mean the absence of a unit. Example: Money/salaries.

sampling

the act of selecting subjects from a population to create a sample. TERM 22

sampling bias

DEFINITION 22 error that occurs from having a nonrepresentative sample from the population TERM 23

sampling

frame

DEFINITION 23 a list of the possible subjects to sample from. (not necessarily all of the originally selected subjects will always fit into a sample frame) TERM 24

sampling design

DEFINITION 24 the methodology used for selecting subjects from the sample frame TERM 25

4 basic random sampling designs

DEFINITION 25 simple random sampling design, cluster random sampling design, stratified random sampling design, systematic random sampling design.

simple random sampling design (SRS)

involves selecting a sample of size n subjects (from N = subjects in sampling frame) from a population where each possible sample of that size has the same chance of being selected. Each subject in the sampling frame has an equal chance of being selected. Paper or electronic random number generator is used to select the subjects from the sampling frame. TERM 27

cluster random sampling design

DEFINITION 27 in this desgin, the sampling frame is able to be broken into clusters by some natural auxiliary variable. The SRS is then used to selecet the clusters one at a time. Of the clusters randomly selected, use all of the subjects in the sample. TERM 28

stratified random sampling design

DEFINITION 28 in this design, the sampling frame is able to be broken into strata by some natural auxiliary variable. This time, the researched will use each stratum by taking a SRS from each one. If the stratum are not the same size, then you must find out what percent of the subjects in this stratum represent that of the sampling frame. Divide the sample number by the sampling frame and multiply this number by the sample size (the selected people) to determine how many subjects to sample from that strata. TERM 29

systematic random sampling design

DEFINITION 29 in this design, the researcher numbers each subject in the sampling frame the same as if the SRS was used. Then you randomly select the first subject from the first N/n observations in the sampling frame. The remaining subjects will be selected by going every kth observation from the first subject selected. Count the number of subjects in the sampling frame. Determine the sample size needed. find N/n and use the highest positive integer k where k is less than or equal to N/n. randomly select the first subject i where i is greater than or equal to one and less than or equal to k. Go every k elements in the sampling frame until the same size of n is gathered.