Research for Business Professionals Exam 2

Sample

A small part of an entire population. Ideally it is representation of all the characteristics of the population.

Sampling

The process of selecting representative units from a total population

Probability Sampling

A type of sampling in which every element in the population being studied has a known chance of being selected for study

Non-Probability Sampling

a sampling technique in which there is no way to calculate the likelihood that a specific element of the population being studied will be chosen

Convenience Sampling

choosing individuals who are easiest to reach

Accidental Sampling

non-probability design in which you use the most readily available persons as subjects. May use snowball sampling. Most commonly used because its convenient and least costly.

Pilot studies

surveys using a limited number of respondents and often employing less rigorous sampling techniques than are employed in large, quantitative studies

Snowball Sampling

recruitment of participants based on word of mouth or referrals from other participants

Purposive Sampling

a biased sampling technique in which only certain kinds of people are included in a sample

Homogenous Sampling

Selecting participants who are very similar in experience, perspective, or outlook

Deviant Case Sampling

a type of nonrandom sample, especially used by qualitative researchers, in which a researcher selects unusual or nonconforming cases purposely as a way to provide greater insight into social processes or a setting

Low external validity

the ability to generalize results from a study of certain individuals to other individuals not being studied

Quota sampling

A nonprobability sampling technique in which researchers divide the population into groups and then arbitrarily choose participants from each group

Proportional Quota Sampling

A sampling method where you sample until you achieve a specific number of sampled units for each subgroup of a population, where the proportions in each group are the same

Non-proportional quota sampling

a type of quota sampling that uses a different quota from the one found in the population of interest because the study's aim is to compare two or more different groups of interest.

Simple Random Sampling

every member of the population has an equal probability of being selected for the sample

Systematic Random Sampling

A method of sampling in which sample elements are selected from a list or from sequential files, with every nth element being selected after the first element is selected randomly within the first interval

Stratified Random Sampling

A form of probability sampling; a random sampling technique in which the researcher identifies particular demographic categories of interest and then randomly selects individuals within each category.

Proportionate Stratified Sampling

sampling method in which elements are selected from strata in exact proportion to their representation in the population

Disproportionate stratified sampling

Sampling in which elements are selected from strata in different proportions from those that appear in the population

Cluster Random Sampling

dividing the total population into groups (or clusters), then using simple random sampling to select which clusters participate; all observations in a selected cluster are included in the sample

Sampling error

The level of confidence in the findings of a public opinion poll. The more people interviewed, the more confident one can be of the results.

Confidence Interval

the range of values within which a population parameter is estimated to lie

Saturation

the state or process that occurs when no more of something can be absorbed, combined with, or added.

Large population the sample size is.....

385

Sampling Frame

A list of everyone in the population

Saturation=

Quantitative Research

What is the first step of data collection in surveys

Develop a list of constructs (everything that needs to be measured)

What is step 2 of data collection in surveys

Determine constructs (Separate constructs from items to be measured)

What is step 3 of data collection in surveys

Create the first variables (Look for related, established survey instruments, potential sources for questions and questionnaires)

What is step 4 of data collection in surveys

Turn constructs into variables (Consult the literature or create open-ended questions)

Closed variables: More specific

Open variables: Better for opinions and attitudes

What is step 5 of data collection in surveys

Tips and ideas on crafting the best possible questions

- Clarity

- Simplicity

- Unbiased

- Intelligible

- Avoid social desirability

- Avoid double-barreled questions

- Avoid double-negatives

- Contingency questions

- Avoid dangling alternatives

- Avoid hypothetical questions

What is step 6 of data collection in surveys

Organize in a manner that attracts and holds participant attention

Bored participants may change attitudes

Attention may decrease over time

Ask demographic questions at the end

- May be more accurate before exposing identity

Questions should be in logical order

Researchers should not use only scale measurements

What is step 7 of data collection in surveys

Create an answer scale

- Scale should range from maximum to minimum

- Consider even or odd response option number

An even number forces a stand-by respondent

- Answers should include all options

What is step 8 of data collection in surveys

Conduct a pilot study to evaluate the instrument

- Pilot study

- Gives a sense of time needed for completion

- Helps determine issues in understanding, sequence, or discomfort

- Participants may offer suggestions

- A researcher can test data entry

Computer-Assisted Telephone Interviewing

a data-collection technique in which a telephone-survey questionnaire is stored in a computer, permitting the interviewer to read the questions from the monitor and enter the answers on the computer keyboard

Virtual Data Collection

Online surveys are very popular and can access a large number of participants in a matter of minutes, with a higher response rate than face-to-face or telephone data collection

Drawbacks: Much of the population has no internet access

- Participants who feel strongly are more likely participate

- The researcher cannot guarantee the respondent's identity

- Sample size

Measurement

The process of assigning values or categories to concepts being studied

Measurement error

The difference between the true value and the value collected by the researcher

Reliability

The consistency of a measure. A reliable instrument gives similar results under consistent conditions

Validity

The extent to which a tool actually measures what it is supposed to measure

Random error

Happens by chance and makes results less consistent

Systematic error

A repeated error that pushes results in the same wrong direction

Tes-retest reliability

Shows whether the same measure gives similar results over time

Interrater reliability

Shows whether different observers or raters agree

Internal consistency

Shows whether items on a survey or test measure the same idea

Face validity

Whether the measure appears to assess the right concept

Content Validity

Whether the measure covers all important parts of the concept

Construct validity

Whether the measure truly reflects the theoretical concept

Criterion validity

Whether the measure relates well to an outside standard or outcome

A measure can be reliable without being _________

Valid

A measure cannot be truly valid if it is not ________

Reliable

Population

The full group the researcher wants to study

Sampling frame

A list or source from which the sample is drawn

Simple random sampling

Everyone has equal chance

Systematic sampling

Select every nth person

Stratified sampling

Divide population into groups, then sample from each

Cluster sampling

select whole groups or clusters

Nonprobability sampling

Not every member has a known chance of being selected

Convenience sampling

select whoever is easiest to reach

Purposive sampling

Choose participants with specific characterisitics

Snowball sampling

Participants help recruit others

Nonprobability sampling is often....

Faster, cheaper, and easier

Quantitive research

Research that collects numerical data and often tests relationships between variables

Experiment

A design in which the researcher manipulates an independent variable to see its effect on a dependent variable

Quasi-experiment

A design that looks like an experiment but does not use full random assignemnt

Questionnaire

A written set of questions used to collect data from participants

Secondary data

Data that already exists and was orginally collected for another purpose

Primary data

Data collected directly by the researcher for the current study

Benefits of Secondary Data

- Saves time & money

- May provide access to large datasets

- Helpful for trend analysis over time

- Useful when collecting primary data is difficult

Disadvantages of Secondary Data

- Researcher did not control how the data was collected

- Important variables may be mising

- Data may be outdated

Definitions may not match the new study

- Quality of the original data may vary

Using U.S. Census data to study income trends is an example of using....

Secondary data

Researchers may collect quantitative data through:

- Surveys

- Structured observations

- Tests

- Existing numerical records

- Experiments

Good questionnaire design includes:

- Clear wording

- Simple language

- One idea per question

- Neutral wording

- Response options that makes sense

Common problems with questionnaires:

- Leading questions

- Double-barreled questions

- Confusing wording

- Too many open-ended questions in a quantitative study

Strengths & Weaknesses of Probability sampling

- Stronger representativeness

- Better for generalization

- Often more time-consuming and expensive

Strengths & Weaknesses of Nonprobability sampling

- Easier to use

- Helpful for explanatory research or hard to reach groups

- More risk of bias

Qualitative research is ______ centered

participant

Major sources of secondary data

- Government databases

- Census data

- Public reports

- Academic databases

- Company records

- Archived surveys

- International organization data

Disadvantages of Secondary Data

- Researcher did not control how the data was collected

- Important variables

- Data may be outdated

- Definitions may not match the new study

- Quality of the original data may vary

When using secondary data researchers should check:

- Who collected the data

- Why it was collected

- When it was collected

- How variables were defined

- Whether it fits the current research question