Lecture 10 Flashcards
Types of questions: Blank
“Enter your age in years” ___
Types of questions: Dichotomous questions (response formats)
Only 2 possible answers
Types of questions: Nominal
“Check how you travel to school/work”: bus car walk other
Types of questions: Ordinal
Rank the following issues from your most important (1) to your least important (4)
Types of questions: Interval
Likert and Semantic
Likert scale
Some statements: Respondents tell their level of agreement to each statement (item) • Strongly disagree (SD) • Disagree (D) • Undecided (U) • Agree (A) • Strongly agree (SA)
Ratings of items can be summed to produce an overall score
Semantic differential
Anchor scale with adjectives that are polar extremes
Ordering items in Self-administered surveys
Begin with the most interesting set of items (want to answer them)
Leave dull demographic items (e.g., age, gender) and more difficult items at the end.
Ordering items in Interview-administered surveys
Begin with easily answered and non threatening questions, then move into more sensitive matters.
High response rate means ________.
less chance of bias
Contingency question
when some questions relevant to some but not all respondents (avoid more than three levels
Matrix questions
Asking a few questions that have the same set of answer categories. Typically, using Likert response.
Snowball Sampling (purposive sampling)
You begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria.
Heterogeneity Sampling (purposive sampling)
We sample for heterogeneity when we want to include all opinions or views, and we aren’t concerned about representing these views proportionately. Another term for this is sampling for diversity
Two Types of Quota Sampling (purposive sampling)
Proportional quota sampling: you want to represent the major characteristics of the population by sampling a proportional amount of each
Nonproportional quota sampling: you specify the minimum number of sampled units you want in each category. here, you’re not concerned with having numbers that match the proportions in the population. You simply want to have enough to assure that you will be able to talk about even small groups in the population
Expert Sampling (purposive sampling)
Expert sampling involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area
Modal Instance Sampling (purposive sampling)
The mode is the most frequently occurring value in a distribution. In sampling, when we do a modal instance sample, we are sampling the most frequent case, or the “typical” case.
Purposive Sampling
We sample with a purpose in mind. We might sample for specific groups or types of people as in modal instance, expert, or quota sampling. We might sample for diversity as in heterogeneity sampling. Or, we might capitalize on informal social networks to identify specific respondents who are hard to locate otherwise, as in snowball sampling. In all of these methods we know what we want – we are sampling with a purpose
Non-probability Sampling
The difference between non-probability and probability sampling is that non-probability sampling does not involve random selection and probability sampling does
Accidental, Haphazard or Convenience Sampling
It is primarily a matter of sampling out of convenience. the problem with all of these types of samples is that we have no evidence that they are representative of the populations we’re interested in generalizing to – and in many cases we would clearly suspect that they are not.
Probability Sampling
is any method of sampling that utilizes some form of random selection
Simple Random Sampling
Use a table of random numbers, a computer random number generator, or a mechanical device to select the sample
Stratified Random Sampling
Also called proportional or quota random sampling, involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup.
Systematic Random Sampling
i. number the units in the population from 1 to N
ii. decide on the n (sample size) that you want or need
iii. k = N/n = the interval size
iv. randomly select an integer between 1 to k
v. then take every kth unit
Cluster (Area) Random Sampling
In cluster sampling, we follow these steps:
i. divide population into clusters (usually along geographic boundaries)
ii. randomly sample clusters
iii. measure all units within sampled clusters
Multi-stage Sampling
The most important principle here is that we can combine the simple methods described earlier in a variety of useful ways that help us address our sampling needs in the most efficient and effective manner possible
