LAST AD RESEARCH TEST

Sampling error

• What occurs when you use an improper method of collecting the sample/or too small a sample size

• Too big of a sampling error will greatly impact the external validity of the research

Probability sampling

Occurs when a method is employed where every unit of the sampling frame (population) is known

Non-probability sampling

Is subjective and haphazard and prone to bias

Simple random sampling

researcher puts the names of everyone in a sample frame and randomly selects names by pulling them randomly

• every sample unit in the sample frame has the same chance of being selected

• the pulling the name out of a hat method

Cluster sampling

The sample is divided into subgroups called clusters, and then the researcher selects a sample from each.The sample is dicided into subgroups called clusters and then the researcher selects a sample from each. This is good when clusters are easily identifed and similar.

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Stratified sampling

• Used when the researcher is working with “skewed populations” and needs to achieve high statistical efficiency.

• Purpose is to achieve statistical efficiency and external validity with your research

Confidence interval

is a range of values used to estimate an unknown population parameter (like a population mean or proportion) based on sample data

• provides a range within which the true parameter is likely to fall, with a certain level of confidence.

Margin of error

• the amount of error that could be present in an estimate, it represents how much the sample estimate might differ from the true population value.

• is half the width of the confidence interval

Variability

the amount of dissimilarity or similarity in a respondents answer to the same question.

• most cases will default to 50/50 unless you have specialized research

Sample size formula

N=Z²(pq)/e²

• n = sample size

• Z = confidence interval

• P = estimate % of population

• Q = 100 - p

• E = acceptable margin of error

Diminishing returns

the principle that as investment in a particular resource increases, the incremental benefit derived from that investment will eventually decrease.

Collection methods are more important in non-probability sampling

True

All You Can Afford method of sampling

Where resources available dictate the sample size.

What is standard deviation?

a measure of the amount of variation of the values of a variable about its mean.

Why is standard deviation important?

gives you a deeper understanding of the variability and reliability of your data, which is crucial for drawing accurate conclusions.

What are datasets?

collections of data, typically organized in a structured way, that is used for analysis, research, or training machine learning models. They are essential for good research.

Types of non-sampling errors

• fieldwork errors

• respondent errors

• Data collection errors

fieldwork errors

sampling errors caused by fieldworkers hired to conduct the research that impact the external and internal validity of the research

intentional fieldwork errors

• falsifying data

• interviewer cheating

• asking questions poorly

unintentional fieldwork errors

these are mistakes

• making subjects nervous

• improper training

• personal biases they don’t know they have

• fatigue

• insufficient time to complete the research (hurrying)

respondent errors

non sampling errors caused by the respondents in the research that impact the external and internal validity of the research

unintentional respondent errors

• misunderstanding

• guessing

• attention loss

• distractions

the best way to avoid is to have quality question design

intentional respondent errors

• speeding

• misrepresenting themselves for compensation

• lying

• inattentive responses

best way to control is with control questions and reverse scales

response rates have plummeted in recent years due to

• the decline of landline phones

• mail is inefficient

• spam filters

• people are inundated with survey requests (burn out)

descriptive analysis

used by marketing researchers to describe typical respondent in detail and reveal useful patterns in their responses. Most common type of market research

inference analysis

used to determine the standard error, null hypothesis, confidence intervals

difference analysis

used to determine statistical differences among a population. Often called market segmentation studies.

association analysis

used to determine if there are correlations between certain variables

relationship analysis

looks for complex relationships between multiple independent variables and a single dependent variable

Understanding descriptive analysis

• based on measures of central tendency

  • mean, median, mode

• Measures of variability is a tabulation of how many times a value appears in the data set

basic difference tests

used to determine if there's a significant difference between the means (average values) of two groups. These tests help researchers decide if the observed difference between the groups is likely due to a real effect or simply random chance. 

In order to be useful to us in marketing research the “differences” in basic difference tests must be…

• statistically significant

  • large enough to really separate into meaningful clusters

• meaningful to the subject

  • important enough to influence a marketing decision

null hypothesis

• a statement or assumption that there is no effect, relationship, or difference between variables in a population.

• suggests that any observed effect in the data is due to random chance rather than a true underlying cause

the importance of null hypothesis

• provides a clear and objective framework for statistical testing, helping researchers avoid drawing incorrect conclusions from their data.

• ensures that claims about relationships or effects are supported by sufficient evidence

who can afford the time and resources to conduct a census?

only the government

internal validity

How certain are we that the observed change in the dependent variable is due to the manipulation of the independent variable and not other factors

external validity

the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables. External validity is the extent to which your results can be replicated to other contexts.

systematic sampling

same thing as random sampling, but the researcher creates a skip interval based on the sample size relative to the population

• makes things easier and less prone to human error

convenience samples

samples drawn from groups that the researcher can easily access

chain referral sampling

• snowballing sample

• require/request that your respondents provide the names of other possible candidates or that they send forward the survey

Purposive sampling

recruiting respondents on social media or forums that they believe are likely to have high concentrations of the desired population

quota sampling

provide the researcher specifics different percentages of demographics they want in the sample and recruit until they are reached