Pr Research Exam 3 Part 2

Front 1

Process in which a sample is drawn from the population of interest

Back 1

Sampling

Front 2

Sample - subset of the population that is representative of the population

Population - an entire group of subjects under investigation

Back 2

Sample v. Population

Front 3

Sample selected from every portion of a population

(ex. age, gender, income, education, etc.)

Back 3

Representative Sample

Front 4

Sampling drawn at random from a population such that all possible respondents or objects have an equal chance of being selected for observation or measurement

Back 4

Probability Sampling

Front 5

Sampling drawn from a population whereby respondents or objects do not have an equal chance of being selected for observation or measurement

Back 5

Non-probability Sampling

Front 6

1. Convenience Sampling (Non-probability)
2. Purposive Sampling (Non-probability)
3. Snowball Sampling (Non-probability)
4. Simple Random Sampling (Probability)
5. Systematic Sampling (Probability)
6. Stratified Sampling (Probability)
7. Cluster Sampling (Probability)

Back 6

Sampling Techniques

Front 7

Probability sampling in which numbers are assigned to each member of a population, a random set of numbers is generated and the only those members having the random numbers are included in the sample. Uses computer-generated random numbers or random numbers table

Back 7

Simple Random Sampling

Front 8

Probability sampling in which which units in a population are selected from an available list at a fixed interval after a random start. Every "nth" number on the list is selected.

Back 8

Systematic Sampling

Front 9

Probability sampling that involves first breaking down the total population into homogeneous subsets (or strata) and then selecting the potential sample at random from the individual strata. Used to get an adequate representations of different sub-groups. Similar within and different between.

Back 9

Stratified Sampling

Front 10

Type of probability sampling that involves first breaking the population into heterogenous subsets (or clusters) and then selecting the potential sample at random from the individual clusters. Different within and similar between.

Back 10

Cluster Sampling

Front 11

Systematic, Scientific, Generalization is possible, Estimate sampling error, Expensive and time consuming

Back 11

Probability Sampling

Front 12

Short hand version, Generalization is impossible, Cannot calculate sampling error, Quick and inexpensive

Back 12

Non-probability Sampling

Front 13

Non-probability sampling where the respondents or objects are chosen because of availability, in which whoever happens to be available at a given time is included in the sample. Haphazard or accidental sample. Contain unknown amount of errors. Useful for pilot study. (ex. mall intercept)

Back 13

Convenience Sampling

Front 14

Non-probability sampling in which individuals are deliberately selected for inclusion based on their specific knowledge, position, characteristics, or relevant dimensions of the population. USed for in-depth analysis related to the central issues

Back 14

Purposive Sampling

Front 15

Non-probability sampling in which individuals who are interviewed are asked to suggest other individuals for further interviewing (respondent referrals). Able to reach rare or highly-specialized groups

Back 15

Snowball Sampling

Front 16

Are computed on quantitative data and provide a way to describe the data. Significance - amount of confidence placed in ensuring that findings truly represent those in the larger population. If alpha error level = .05 then significance is 95% (1.00 - .05 = .95)

Back 16

Quantitative Data Analysis: Statistics

Front 17

Reduction and simplification of the numbers representing research, to ease interpreting the results. USed to obtain data distributions (frequency and percentage) and summary statistics (central tendency: mode, median, mean and dispersion: range, variance and standard deviation)

Back 17

Descriptive Statistics

Front 18

Statistical tests that allow a researcher to say within a certain degree of confidence whether variables or groups truly differ in there reponse to a message

Back 18

Inferential Statistics

Front 19

Used to determine the linear relationship between two continuous (interval or ratio) variables

Back 19

Correlation

Front 20

Inferential Statistic. Test of significance when number of observations is less than 100. Used to compare the mean scores between two groups. Independent variables must be categorical variables and dependent variables must be continuous variables (interval or ratio). One categorical and one continuous.

Back 20

t-Test

Front 21

Inferential Statistic. Test of Significance. Used to determine the relationship between two categorical variables

Back 21

Chi-square