Research Marketing exam #2 review

Definition of Sampling Units

are target population elements actually available to be used during the sampling process.

Simple Random Sampling

is a probability sampling procedure. With this approach, every sampling unit has a known and equal chance of being selected. For example, an instructor could draw a sample of 10 students from among 30 students in a marketing research class.

Systematic Random Sampling 

is similar to simple random sampling but requires that the defined target population be ordered in some way, usually in the form of a customer list, taxpayer roll, or membership roster. In research practices, systematic random sampling has become a popular method of drawing samples. Systematic random sampling is less costly because it can be done relatively quickly. It can also create a sample of objects or prospective respondents that is very similar in quality to a sample drawn using simple random sampling.

Skip Interval

is determined by dividing the number of potential sampling units in the defined target population by the number of units desired in the sample. The formula is Skip Interval = Defined target population list size/Desired sample size

Stratified Random Sampling

involves the separation of the target population into different groups, called strata, and the selection of samples from each stratum. Stratified random sampling is similar to segmentation of the defined target population into smaller, more homogeneous sets of elements.

Cluster Sampling

is similar to stratified random sampling, but is different in that the sampling units are divided into mutually exclusive and collectively exhaustive subpopulations called clusters. Each cluster is assumed to be representative of the heterogeneity of the target population. Examples of possible divisions for cluster sampling include customers who patronize a store on a given day, the audience for a movie shown at a particular time (e.g., the matinee), or the invoices processed during a specific week

Convenience Sampling

 is a method in which samples are drawn based on convenience. For example, interviewing individuals at shopping malls or other high-traffic areas is a common method of generating a convenience sample.

Judgement Sampling

sometimes referred to as purposive sampling, respondents are selected because the researcher believes they meet the requirements of the study. For example, sales representatives may be interviewed rather than customers to determine whether customers’ wants and needs are changing or to assess the firm’s product or service performance.

Quota Sampling

 involves the selection of prospective participants according to prespecified quotas for either demographic characteristics (e.g., age, race, gender, income), specific attitudes (e.g., satisfied/dissatisfied, liking/disliking, great/marginal/no quality), or specific behaviors (e.g., regular/occasional/rare customer, product user/nonuser). The purpose of quota sampling is to assure that prespecified subgroups of the population are represented.

Snowball Sampling

involves identifying a set of respondents who can help the researcher identify additional people to include in the study. This method of sampling is also called referral sampling because one respondent refers other potential respondents. Snowball sampling typically is used in situations where (1) the defined target population is small and unique and (2) compiling a complete list of sampling units is difficult. Consider, for example, researching the attitudes and behaviors of people who volunteer their time to charitable organizations like the Children’s Wish Foundation.

Area Sampling

In area sampling, the clusters are formed by geographic designations. Examples include metropolitan statistical areas (MSAs), cities, subdivisions, and blocks. Any geographical unit with identifiable boundaries can be used. The researcher has two additional options: the one-step approach or the two-step approach. When deciding on a one-step approach, the researcher must have enough prior information about the various geographic clusters to believe that all the geographic clusters are basically identical with regard to the specific factors that were used to initially identify the clusters.

Using Probability sampling methods

each sampling unit in the defined target population has a known probability of being selected for the sample. The actual probability of selection for each sampling unit may or may not be equal depending on the type of probability sampling design used. Specific rules for selecting members from the population for inclusion in the sample are determined at the beginning of a study to ensure (1) unbiased selection of the sampling units and (2) proper sample representation of the defined target population. Includes: Simple random sampling, systematic random sampling, stratified random sampling, cluster sampling

Nonprobability sampling methods

 the probability of selecting each sampling unit is not known. Therefore, sampling error is not known. Selection of sampling units is based on intuitive judgment or researcher knowledge. The degree to which the sample is representative of the defined target population depends on the sampling approach and how well the researcher executes the selection activities. Includes: Convenience sampling, judgment sampling, quota sampling, snowball sampling

Determining sample size (formula sheet)

Estimating an average/mean

Use when: the research question asks for an average numerical value, such as average dollars spent or an average rating. If the study asks – How much, or what average, Information needed – confidence level, margin of error in the original units, and estimated standard deviation 

Estimating proportion vs. percentage (what is involved in each)

Use when: the research question asks for the percentage of a population in a category, usually based on a yes/no or categorical response. If the study asks — What percentage, share, or proportion, Information needed — Confidence level, margin of error in percentage points, and expected proportion

Sampling plan

is the blueprint to ensure the data collected is representative of the population. A good sampling plan includes the following steps: Page 163 (1) define the target population, (2) select the data collection method, (3) identify the sampling frames needed, (4) select the appropriate sampling method, (5) determine necessary sample sizes and overall contact rates, (6) create an operating plan for selecting sampling units, and (7) execute the operational plan.

Sampling Frame

After defining the target population, the researcher develops a list of all eligible sampling units, some common sources of sampling frames are lists of registered voters and customer lists from magazine publishers or credit card companies. There also are specialized commercial companies (for instance, Survey Sampling Inc., American Business Lists Inc., and Scientific Telephone Samples) that sell databases containing names, addresses, and telephone numbers of potential population elements. 

Scale points

Designated degrees of intensity assigned to the responses in a given questioning or observation method. For example, a retailer might want to know how important a preselected set of store or service features is to consumers in deciding where to shop. Scale measurement involves assigning a set of scale descriptors to represent the range of possible responses to a question about a particular object or construct.

Nominal

is the most basic and least powerful scale design. With nominal scales, the questions require respondents only to provide some type of descriptor as the response. 

Ordinal

more powerful than nominal scales. This type of scale enables respondents to express relative magnitude between the answers to a question and responses can be rank-ordered in a hierarchical pattern.

Interval

can measure absolute differences between scale points. That is, the intervals between the scale numbers tell us how far apart the measured objects are on a particular attribute.

Ratio

are the highest level scale because they enable the researcher not only to identify the absolute differences between each scale point but also to make absolute comparisons between the responses.

Mean

 the average value within the distribution and is the most commonly used measure of central tendency. The mean tells us, for example, the average number of cups of coffee the typical student drinks during finals to stay awake.

Median

the middle value of the distribution when the distribution is ordered in either an ascending or a descending sequence. For example, if you interviewed a sample of students to determine their coffee-drinking patterns during finals, you might find that the median number of cups of coffee consumed is 4.

Mode

the value that appears in the distribution most often. For example, the average number of cups of coffee students drink per day during finals may be 5 (the mean), while the number of cups of coffee that most students drink is only 3 (the mode).

Standard deviation

describes the average distance of the distribution values from the mean value. The difference between a particular response and the distribution mean is called a deviation.

Range

defines the spread of the data. It is the distance between the smallest and largest values of the variable. Another way to think about it is that the range identifies the endpoints of the distribution of values.

Constructs

is an unobservable concept that is measured indirectly by a group of related variables. Thus, constructs are made up of a combination of several related indicator variables that together define the concept being measured.

What is measurement?

is the process of developing methods to systematically characterize or quantify information about persons, events, ideas, or objects of interest. As part of the measurement process, researchers assign either numbers or labels to phenomena they measure.

Likert Scale

asks respondents to indicate the extent to which they either agree or disagree with a series of statements about a subject. Usually the scale format is balanced between agreement and disagreement scale descriptors.

Semantic Differential Scale

This scale is unique in its use of bipolar adjectives (good/bad, like/dislike, competitive/ noncompetitive, helpful/unhelpful, high quality/low quality, dependable/undependable) as the endpoints of a continuum.

Graphic Rating Scales

use a scaling descriptor format that presents a respondent with a continuous line as the set of possible responses to a question.

Double-barreled questions

includes two or more different attributes or issues in the same question, but responses allow respondent to comment on only a single issue.

Leading Questions

 introduces bias and often influences the way a respondent answers a question. The question below likely influences some respondents to check the Agree option because it indicates what “Experts” think.

Loaded questions

is a situation where the question/setup suggests a socially desirable answer or involves an emotionally charged issue.

Ambiguous questions

involve a situation in which possible responses can be interpreted a number of different ways. When this is present, it typically creates confusion among respondents about how to respond.

Complex questions

are situations in which the question is worded in a way the respondent is not sure how they are supposed to respond.

A Double Negative Question

involves a situation where the question/setup contains two negative thoughts in the same question. Double negative expressions in a question create cognitive confusion and respondents find it difficult to understand the question and therefore respond correctly.

Structured questions 

are closed-ended questions that require the respondent to choose from a predetermined set of responses or scale points. Structured formats reduce the amount of thinking and effort required by respondents, and the response process is faster. In quantitative surveys, structured questions are used much more often than unstructured ones.  

Unstructured questions

are open-ended questions that enable respondents to reply in their own words. There is no predetermined list of responses available to aid or limit respondents’ answers. For example, respondents might be asked to respond to the following: “In the space provided, please type the reasons why you like the banking services offered at (current primary banking relationship).”

Skip questions

 can appear anywhere within the questionnaire and are used if the next question (or set of questions) should be responded to only by respondents who meet a previous condition. A simple expression of a skip command might be: “If you answered ‘yes’ to question 5, skip to question 9.”

Screening questions

(also referred to as screeners or filter questions) are used on most questionnaires. Their purpose is to identify qualified prospective respondents and prevent unqualified respondents from being included in the study

Steps in the questionnaire design process

Step 1: Confirm research information objectives and data requirements 

Step 2: Select appropriate data collection method 

Step 3: Develop questions and scaling 

Step 4: Determine layout and evaluate questionnaire 

Step 5: Obtain initial client approval 

Step 6: Pretest, revise, and finalize questionnaire 

Step 7: Implement the survey

Unanswerable questions

either because the respondent does not have access to the information needed or because none of the response choices apply to the respondent. Examples would be: “What was your parents’ yearly after-tax income last year?” or “How much did you spend on food last month?” In general, respondents do not know the answer to this type of question but will still attempt to answer it. They give an “informed guess” based on what they think is about right.

Transition phrase

“Now just a few more questions so that we can combine your responses with those of the other people taking part in this study.” serves two purposes. First, it communicates to the respondents that a change in their thinking process is about to take place. They can clear their minds before thinking about their personal data. Second, it indicates the task of completing the survey is almost over.

Sensitive topics

include income, sexual beliefs or behaviors, medical conditions, financial difficulties, alcohol consumption, and so forth. These types of behaviors are often engaged in but may be considered socially unacceptable. As an example, consider the question below:

Response order bias

(also referred to as sequence bias) occurs when the order of the questions, or of the closed-end responses to a particular question, influences the answer given. Answers that appear at the beginning or the end of a list tend to be selected most often. With numeric alternatives (prices or quantities)

Attention check questions

ensure respondents are reading each question or scale item carefully, which promotes high-quality data and enables researchers to feel confident in postcollection data analysis and data cleaning procedures. There are many different ways of inserting attention check questions in self-report surveys. For example, insert a question into the survey flow that asks for a specific answer [e.g., What color is grass? or in a multiple item scale design including a scale item that specifically ask the respondent to select a particular scale point (e.g., Please select “neither agree nor disagree”).

Pretest

is a descriptive research activity representing a small-scale investigation of 10–30 subjects that are representative of the main study’s defined target population but focus on a specific subcomponent of the main study.

Pilot Study

is a small-scale version of the intended main research study, including all the subcomponents that make up the main study, such as the data collection and analysis, from about 50 to 200 respondents who are representative of the main study’s defined target population. A pilot study often serves as a guide for conducting a larger main study. 

Difference between quantitative and qualitative

is textual, visual, or perhaps from sensors measuring some phenomenon, rather than numerical. While the goal of quantitative analysis is quantifying the magnitude of variables and relationships, or explaining causal relationships, understanding is the goal of qualitative analysis. 

A second contrast between the two kinds of analysis is that qualitative analyses tend to be ongoing and iterative. This means the data is often analyzed and sometimes separated into categories or groups as it is collected, which may affect further data collection efforts in terms of who is sampled and what questions are asked. Another difference between the methods is that quantitative analyses are guided entirely by the researchers, while good qualitative researchers employ member checking

Member checking

involves asking key informants to read the researchers’ report to verify the story they are telling about the focal problem or situation is accurate.

Data reduction

The most systematic method of analysis is to read through transcripts and develop categories to represent the data. When similar topics are encountered, they are coded similarly. Researchers may simply write codes in the margins of their transcripts. 

Comparison

first occurs as researchers identify categories. Each potential new instance of a category or theme is compared to already coded instances to determine if the new instance belongs in the existing category.

Integration

is the process through which researchers build theory that is grounded, or based on the data collected. The idea is to move from the identification of themes and categories to the development of theory.

Iteration

 means working through the data in a way that permits early ideas and analyses to be modified by choosing cases and issues in the data that will permit deeper analyses. The iterative process may uncover issues that the already collected data do not address. In this case, the researcher will collect data from more informants, or may choose specific types of informants they believe will have the knowledge to answer questions that arise during the iterative process.

Negative case analysis

which means that they deliberately look for cases and instances that contradict the ideas and theories that they have been developing. Negative case analysis helps to establish boundaries and conditions for the theory that is being developed by the qualitative researcher.

Credibility

 to describe the rigor and believability established in qualitative analysis. Credibility is also increased when key informants and other practicing qualitative researchers are asked to review the analyses. As mentioned, soliciting feedback from key informants or member checking strengthens the credibility of qualitative analysis.

Peer review

also strengthens credibility. Key informants and external qualitative methodology and topic area experts often question the analyses, push researchers to better clarify their thinking, and occasionally change key interpretations in the research.

Verbatims

 or quotes from research participants, are often used in the textual report as well as in data displays. When they are well chosen, verbatims are a particularly powerful way to underscore important points because they express consumer viewpoints in their own voice. Video verbatims can be used in live presentations.

Triangulation

is the technique most often associated with credibility in qualitative research. It requires that research inquiry be addressed from multiple perspectives. Several kinds of triangulation are possible:Multiple methods of data collection and analysis. Multiple data sets.