MKT310 Concept Check 2

drop a case completely if...

half or more of the responses are missing

Precision

the degree of error in an estimate of a population parameter

Output viewer

•shows you tables of statistical output and any graphs you create.

response rate

# of completed interviews with responding units / # of eligible responding units in the sample

examples of paired sample t-test

- before and after measures - applying same measure to different objects Good for when comparing 2 means when the scores for both variables are provided by the same sample

examples of independent sample t-test for means

- satisfaction ratings: gen Z vs Millennials - age in years: customers vs non-customers - two groups of customers

uses of frequency analysis

- univariate categorical analysis - identify blunders and cases with excessive nonresponse - identify outliers - identify the median

A correct crosstab analysis has...

1 -- cleaned up cross tab table 2 -- test for statistical significance 3 -- measure of strength of association 4 -- interpret the results

3 methods for diagnosing nonresponse error

1) contact a sample of nonrespondents 2) compare respondent demographics against known demographics of population 3) conduct an analysis of late responders vs early responders

Primary tasks in the editing process

1) convert all responses to consistent units 2) assess degree of nonresponse 3) check consistency across responses 4) look for evidence that the respondent wasn't really thinking about his or her answers 5) verify that branching questions were followed correctly

Developing the Sampling Plan 6 Steps

1) determine the target population 2) identify the sampling frame 3) select a sampling procedure 4) determine the sample size 5) select the sample elements 6) collect the data from the designated elements

To determine how big of a sample size you need, what 3 pieces of information do you need?

1) how homogenous (similar) the population is on the characteristic to be estimated 2) how much precision is needed in the estimate 3) how confident we need to be that the true value falls within the precision range established

converting a continuous measure to a categorical measure

1) median split 2) cumulative % breakdowns 3) two box technique

2 types of sample designs

1) nonprobability samples 2) probability samples

10 Steps for Developing Questionnaire

1) specify what information will be sought 2) determine method of administration 3) determine content of individual questions 4) determine form of response to each question 5) determine wording of each question 6) prepare dummy tables 7) determine question sequence 8) determine appearance of questionnaire 9) develop recruiting message 10) reexamine & pretest

Parameter

A characteristic or measure of a POPULATION - P & P

Statistics

A characteristic or measure of a SAMPLE - We calculate statistics from sample data to estimate the populations parameters - S & S

Constant-sum method

A comparative-ratings scale in which an individual divides some given sum among two or more attributes on a basis such as importance or favorability.

histogram

A form of bar chart on which the values of the variable are placed along the x-axis and the absolute or relative frequency of the values is shown on the y-axis.

Snowball sample

A judgement sample that relies on the researcher's ability to locate an initial set of respondents with the desired characteristics -- Ie. for a new product only a few may have used it yet -- reach out to those and interview them and ask if they know anyone else who has used the product

Self-report

A method of assessing attitudes in which individuals are asked directly for their beliefs about or feelings toward an object or class of objects.

Filter questions

A question used to determine if a respondent is likely to possess the knowledge being sought. It is also used to determine if an individual qualifies as a member of the defined population.

Nonprobability samples

A sample that relies on personal judgement in the element selection process It is IMPOSSIBLE to assess the degree of sampling error A) Convenience aka accidental B) Judgement C) Quota

Graphic rating scale

A scale in which individuals indicate their ratings of an attribute typically by placing a check at the appropriate point on a line that runs from one extreme of the attribute to the other.

Itemized-ratings scale

A scale on which individuals must indicate their ratings of an attribute or object by selecting the response category that best describes their position on the attribute or object. 1) summated ratings 2) semantic differential

Comparative-ratings scale

A scale requiring subjects to make their ratings as a series of relative judgments or comparisons rather than as independent assessments. 1) Constant-sum method

Summated-ratings scale

A self-report technique for attitude measurement in which respondents indicate their degree of agreement or disagreement with each of a number of statements.

Dummy table

A table (or figure) used to show how the results of an analysis will be presented.

Recall loss

A type of error caused by a respondent's forgetting that an event happened at all.

Telescoping error

Error that results from the fact that most people remember an event as having occurred more recently than it did.

What type of data analysis should we use for this question: Does being referred by a doctor to AFC lead to greater usage of the therapy pool?

Crosstabs why? - 2 Categorical variables: (1) Doctor referral (yes, no) = (2) Pool usage (yes, no) doctor ref. = independent/causal variable pool usage = dependent/outcome variable

Sampling error

Difference between results for the sample and what would be true for the population. Isn't usually the biggest problem Alleviate: - increase sample size

Random error

Error in measurement due to temporary aspects of the person or measurement situation that affects the measurement in irregular ways

Systematic error

Error in measurement that is also known as constant error since it affects the measurement in a constant way.

Response order bias

Error that occurs when the response to a question is influenced by the order in which the alternatives are presented.

Classification Information

Information used to classify respondents, typically for demographic breakdowns

Any where options are: not at all important, somewhat important, very important, etc.

Interval

What is your degree of satisfaction with UW-Madison gym offerings? Not at all satisfied Slightly satisfied Moderately satisfied Very satisfied

Interval

Where there is a list of items and you circle the number from unfavorable -> favorable for each item on the list

Interval

Ordinal Scale

Measurement in which numbers are assigned to data on the basis of some order (for example, more than, greater than) of the objects.

Nominal Scale

Measurement in which numbers are assigned to objects or classes of objects solely for the purpose of identification.

Interval Scale

Measurement in which the assigned numbers legitimately allow the comparison of the size of the differences among and between members but we cannot compare the absolute magnitude and there is no zero point.

Ratio Scale

Measurement that has a natural, or absolute, zero and therefore allows the comparison of absolute magnitudes of the numbers.

recording error

Mistakes made by humans or machines in the process of recording respondents' communication- or observation-based data.

Open ended questions

Nominal

What region from the US are you

Nominal

Which of the drinks on the following list do you like? Check all that apply

Nominal

How many fitness platforms do you currently subscribe to? 0 1 2 3 4 5+

Ordinal

How much are you willing to spend on a subscription-based fitness platform? $0-$9.99 $10-$19.99

Ordinal

How would you describe your family income level? Lower class Lower-middle class Upper-middle class Upper class

Ordinal

Rank the drinks according to your degree of liking. 1 = most preferred and 6 = least preferred

Ordinal

On average, how many episodes of TV show do you watch in one sitting? 1-2 3-4 5-6 7+

Ordinal NOT Interval

Please select your age 18 or under 19 20

Ordinal NOT Nominal

Measurement

Rules for assigning numbers to objects in such a way as to represent quantities of attributes.

What type of data analysis would you use for this question: Is there a correlation between age and fees paid?

Pearson product moment correl coeff Why? - age = continuous - fees paid = continuous

How often do you do your own laundry? ______ time(s) per month

Ratio

In the past 7 days how many 12oz servings of each soda have you consumed?

Ratio

Semantic-differential scale

Self-report technique for attitude measurement in which the subjects are asked to check which cell between a set of bipolar adjectives or phrases best describes their feelings toward the object.

Reliability

The ability of a measure to obtain similar scores for the same object, trait, or construct across time, across different evaluators, or across the items forming the measure.

Target Information

The basic information that addresses the subject of the study

Validity

The extent to which differences in scores on a measuring instrument reflect true differences among individuals, groups, or situations in the characteristic that it seeks to measure or true differences in the same individual, group, or situation from one occasion to another, rather than systematic or random errors.

*Target Population vs Target Frame

The target population is all cases that meet designated specifications for membership in the group. The sampling frame is the list of population elements from which a sample will be drawn; the list could consist of geographic areas, institutions, individuals, or other units.

Question Order Bias

The tendency for earlier questions on a questionnaire to influence respondents' answers to later questions.

AFC finds data that nationwide the average age of people who visit fitness centers is 40 years old compared to their average age of 68.6. •What test should AFC use to determine if the means are truly different?

This is a continuous measure and an individual variable therefore A one-sample t-test should be used.T he average age of people who attend fitness centers is 40 years old compared to AFC's average age of 68.6. AFC could then use a one-sample t-test to determine if the means are truly different.

Pretest

Use of a questionnaire (or observation form) on a trial basis in a small pilot study to determine how well the questionnaire (or observation form) works.

Split-ballot technique

Used for combatting response bias in which researchers use multiple versions of a survey, with different wordings of an item or different orders of response options.

cross tabs step 2: testing for statistical significance with the pearson chi-square (x^2) test of independence

a commonly used statistic for testing the null hypothesis that categorical variables are independent of one another

codebook

a document that contains explicit directions about how the data from data collection forms are coded in the data file

cramer's v

a measure of the strength of association numbers range from 0 to 1 & closer to 1 = stronger the relationship

sample stdev

a measure of the variation of responses on a variable

coefficient of multiple determination (R^2)

a measure representing the relative proportion of the total variation in the dependent variable that can be explained or accounted for by the fitted regression equation When there is only 1 predictor variable this is the coefficient of determination

cross tabulation

a multivariate technique used for studying the relationship between 2+ categorical variables considers the joint distribution of sample elements across variables

confidence interval

a projection of the range within which a population parameter will lie at a given level of confidence, based on a statistic obtained from a probabilistic sample only accounts for sampling error

Probability: C) Stratified sample

a sample in which 1) the population is divided into mutually exclusive and exhaustive subgroups and 2) a probabilistic sample of elements is chosen independently from each subset homogenous within, heterogenous between

Probability samples

a sample in which each target population element has a nonzero chance of being included in the sample sampling error CAN be estimated A) simple random B) systematic C) stratified D) cluster (including area)

regression analysis

a statistical technique used to derive an equation representing the influence of a single or multiple independent variable(s) on a continuous dependent/outcome variable

chi square goodness of fit test

a statistical test to determine whether some observed pattern of frequencies corresponds to an expected pattern categorical variables

independent sample t-test for means

a technique commonly used to determine whether two groups differ on some characteristic assessed on a continuous measure Analysis of a continuous measure with a categorical measure as the grouping variable

paired sample t-test

a technique for comparing two means when scores for both variables are provided by the same sample

Census

a type of sampling plan in which data are collected from or about each member of a population

significance

acceptable level of error

Population

all cases that meet designated specifications for membership - Requirements = population ELEMENTS

why use multivariate analysis?

allows researchers a closer look at their data than is possible with univariate analyses

response error

although the individual participates in the study, he or she provides an inaccurate response, consciously or subconsciously Alleviate: - match the background characteristics of interviewer and respondent - avoid ambiguous words and questions - avoid leading questions - avoid unstated alternatives

outlier

an observation so different in magnitude from the rest of the observations that the analyst chooses to treat it as a special case

why use probability sampling?

because you can assess the level of sampling error BUT this is not the only kind of error that can occur

Area sample

cluster sampling where areas serve as the primary sampling units -- using maps, the population is divided

always calculate percentages in the direction of the ______

causal variable

two box technique

converting a INTERVAL LEVEL RATING SCALE into a categorical measure the percentage of respondents choosing one of the top two positions on a rating scale is reported

cumulative % breakdown

converting a continuous measure to a categorical measure categories are formed based on cumulative percentages obtained from frequency analysis

median split

converting a continuous measure to a categorical measure split the continuous measure @ its median value REMEMBER -- on a table you go to the cumulative % and find the one that contains 50%. If there is 40% and 64%, you pick 64 because it contains 50th percentile

frequency analysis

counting the number of cases that fall into each of the possible response categories - Univariate analysis - Categorical

double entry

data entry procedure in which data are entered separately by two people in two data files are compared for discrepancies

Confidence

degree to which one can feel confident that an estimate approximates the true value

Probability: A) Simple random sample

each unit included in the population has a known and equal chance of being selected for the sample

coding FACTUAL open ended questions/items

easier to code because it is a concrete/factual response. ie: the question "what year were you born" is coded as the year ie: the question "how many times have you eaten at Wendys?" you just code the number

coding close ended questions/items

easier, usually yes (1), no (0) -- works like this with check all that appluy

Non-coverage error

error that arises because of failure to include qualified elements of the defined population in the sampling frame Alleviate: - improve sampling frame using other sources - Adjust the results by appropriately weighing the subsample results

item nonresponse

error that arises when a responded agrees to an interview, but refuses or is unable to answer specific questions

office errors

errors that arise when coding, tabulating, or analyzing the data Alleviate:

Probability: B) Systematic sample

every kth element in the population is selected

how to find the median

find the cumulative percentage that includes 50%

null hypothesis

hypothesis that a proposed result is NOT true for the population

alternative hypothesis

hypothesis that proposed result is true for the population

What type of data analysis would you use for this question: Does utilizing the exercise circuit lead to increased number of visits to the center?

independent sample t-test for means why? - exercise circuit = categorical (yes, no) - # of visits (continuous)

continuous measures

interval & ratio

if the p value is _____ we reject the null hypothesis that the values are independent of one another

less than the significance level ie. if p-value = 0.002 p-value < 0.05

coding EXPLORATORY open ended questions/items

much more difficult: Steps to code 1) identify usable responses 2) develop categories for responses 3) sort responses into categories 4) assess degree of agreement btwn coders

categorical measures

nominal & ordinal NO Cats

total sampling elements (TSE)

number of population elements that must be drawn from the pop and included in the initial sample pool in order to end up with the desired sample size

blunders

office errors that occur during editing, coding, or data entry

What type of data analysis would we use for this question: Do the mean attribute importance levels, provided by the same respondents, differ from one another?

paired sample t-test

Nonprobability: A) Convenience aka accidental sample

population elements are included in the sample because they are readily available (we don't know if they are actually representative of the pop) right place @ right tome

p-value

probability of OBTAINING A GIVEN RESULT if the null is true NOT probability that null is true

Data editor

purpose: show you a portion of data values you are working with. It can also be used to redefine the characteristics of variables (change the type, add labels), create new variables, and enter data by hand Gives you 2 views 1) data view 2) variable view

primary sources of nonresponse error

refusals not-at-homes It is better to work hard at generating responses from a smaller sampling pool than to start with a much larger sampling pool and ignore potential nonresponse error The response rate on a project serves as an indicator of the overall quality of a data collection effort

What type of data analysis would you use to answer this question: What are some of the factors that drive revenues at AFC?

regress revenues on 1) member age and the importance of 2) health, 3) social aspects, 4) physical enjoyment, and 5) specific medical concerns

If p < a in a paired sample t-test what do we do?

reject the null hypothesis that the means are the same and tentatively accept that the means are different

Nonprobability: C) Quota sample

sample chosen so that the proportion of sample elements with certain characteristics is about the same proportion of the elements with the characteristics in the target population (most online panels) a "quota" representing these characteristics is established (e.g., 25 Wisconsin residents between the ages of 20 and 29; 25 Illinois residents between the ages of 20 and 29; 35 Minnesota residents between the ages of 30 and 39; etc.) so that when the sample is complete it will mirror the population on the key characteristics

Nonprobability: B) Judgement sample

sample elements are handpicked because they are expected to serve the research purpose

Sample results equation

sample results = truth + (sampling , noncoverage, nonresponse, response, recording, & office errors)

Probability: D) Cluster (area) sample

sampling plan in which (1) the parent population is divided into mutually exclusive and exhaustive subgroups and (2) a random sample of one or more subgroups (clusters) is selected heterogenous within, homogenous between

Sample

selection on a subset of elements from a larger group of objects - The simpler the definition of the target definition, the easier & less costly it will be to find the sample pop

pearson product moment correlation coefficient

statistic that indicates the degree of linear association between two continuous variables range: -1 to 1

descriptive statistics

statistics that describe the distribution of responses on a variable

Sampling error

the difference between the results obtained from a sample and the results that would have been obtained had the info been gathered about every member in the pop - deceased by increasing sample size - can be estimated - usually less troublesome than other kinds of error

Nonresponse error

the failure to obtain data from some elements of the population that were selected for the sample Alleviate: - convince respondent of the importance of their participation - frame the study to enhance interest - keep survey as short as possible - guarantee anonymity - train interviewers well - personalize recruiting message - use an incentive - send follow-up surveys

editing

the inspection and correction of data received from each element of the sample (or census)

Sampling frame

the list of population elements from which the sample will be drawn Ex: customer database, member directories, & lists developed by data compilers perfect sampling frames do not exist

Sampling interval (k)

the number of population elements to count (k) when selecting the sample members in a systematic sample

data aggregation

the process of creating summary data

coding

the process of transforming raw data into symbols (usually numbers)

optical scanning

the use of scanner technology to "read" responses on paper surveys and to store these responses in a data file

hypothesis testing

to tell if a particular result in the sample represents the true situation in the population

one sample t-test

used to compare a sample mean against an external standard

sample mean

x̅ mean value of the responses on a variable

Key Considerations with Response Error

•Does the respondent understand the question? •Does the respondent know the answer to the question? •Is the respondent willing to provide the true answer to the question? •Is the wording of the question or the situation in which it is asked likely to bias the response?

Data view

•Each row represents a unit of observation, sometimes also referred to as a "record" or in SPSS as a "case." •Each column represents a variable. All of the data in a column must be of the same "type," either numeric or string (also called "character").

Variable view

•In the Variable View you can see and edit the information that defines each variable in your data set: each column of the Data View is described by a row of the Variable View.