Research and PR Practice

Conceptual Framework for PR Research

1. Environmental Scanning
Referred to as the gathering of intelligence about publics and environmental forces ..... these activities are conceptually distinct from performance control feedback, program adjustment feedback, and organizational adaption feedback... these feedback loops are conceptual representations in an open-systems model of the three types of program evaluation that practitioners use to measure the preparation, implementation, and impact of public relations programs.
Scanning Research (SR) is different ... (it is) a part of problem-defining stage of PR Planning that moves through the three phases of problem detection, exploration and description (Dozier, 1986) .... (it is) a form of systems input ... inherently open-ended and exploratory in nature ..... The strategic function of scanning is early detection of emerging problems as well as quantification of existing or known problems in the environment ...
Companies that scan the entire environment and aggregate the results to make informed assessments will have an advantage to those that compartmentalize their scanning - one person look at blogs, another at media, yet another at activist groups and so on (blog.holtz.com)
SR is suitable for qualitative research techniques, including focus-group studies, and questionnaires of specific publics or a broad cross section of many publics.

2. Evaluation Research
ER is designed to determine the impact of PR programs using problem description from ES as baseline data (Dozier, 1984a, 1984b). ER can be divided into three levels (Cutlip, Center, and Broom, 1985):
i. Preparation Evaluation - subdivided into three components -
(a) adequacy of background information,
(b) appropriateness of message content and organization, and
(c) quality of message presentattions (style, format, and presentation)
This level of evaluation can be conducted internally or externally by research firms; most common services are message-testing and focus-group studies... Message-testing may be in the form of qualitative formative evaluation. Focus-groups provide a useful mechanism for testing messages at the concept stage. More structured experimental designs, involving large numbers of subjects from the target public and quantified measures of reactions to alternative message and media strategies, provide powerful mechanisms for follow-up on concepts tested through qualitative techniques.
Readibility tests and formulas are used to determine the difficulty of reading written material (Broom & Dozier, 1990). Characteristics such as word length and sentence length are used to determine the score of a writing sample which can be compared against established standards of readibility.
ii. Implementation Evaluation (IE)
IE includes measures of messages sent (distribution), messages placed (coverage), messages received (circulation, reach), and a number of messages attended to by target publics (Cutlip et. al. 1985). ... internal and external placement tracking system (clip file studies, reach analyses, etc.)
iii. Impact Evaluation
Impact of PR programs involve the maintenance of change - among clearly defined target publics and management of the practitioner's organization - of awareness or knowledge levels, attitudes and opinions, and behavior and behavioral predispositions. ... the effectiveness of PR programs is described using the domino metaphor ... a message is assumed to bring about a change in knowledge, which then causes a change in attitude or opinion, which in turn causes a change in behavior or behavioral change.

Survey Research -
Probabilistic Sampling
A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen.
Some Definitions
N = the number of cases in the sampling frame
n = the number of cases in the sample
NCn = the number of combinations (subsets) of n from N
f = n/N = the sampling fraction

Simple Random Sampling
The simplest form of random sampling is called simple random sampling.
Objective: To select n units out of N such that each NCn has an equal chance of being selected.
Procedure: Use a table of random numbers, a computer random number generator, or a mechanical device to select the sample.
How do we select a simple random sample? Let's assume that we are doing some research with a small service agency that wishes to assess client's views of quality of service over the past year. First, we have to get the sampling frame organized. To accomplish this, we'll go through agency records to identify every client over the past 12 months. If we're lucky, the agency has good accurate computerized records and can quickly produce such a list. Then, we have to actually draw the sample. Decide on the number of clients you would like to have in the final sample. For the sake of the example, let's say you want to select 100 clients to survey and that there were 1000 clients over the past 12 months. Then, the sampling fraction is f = n/N = 100/1000 = .10 or 10%.
Here's a simple procedure that's especially useful if you have the names of the clients already on the computer. Many computer programs can generate a series of random numbers. Let's assume you can copy and paste the list of client names into a column in an EXCEL spreadsheet. Then, in the column right next to it paste the function =RAND() which is EXCEL's way of putting a random number between 0 and 1 in the cells. Then, sort both columns -- the list of names and the random number -- by the random numbers. This rearranges the list in random order from the lowest to the highest random number. Then, all you have to do is take the first hundred names in this sorted list. pretty simple. You could probably accomplish the whole thing in under a minute.
Simple random sampling is simple to accomplish and is easy to explain to others. Because simple random sampling is a fair way to select a sample, it is reasonable to generalize the results from the sample back to the population. Simple random sampling is not the most statistically efficient method of sampling and you may, just because of the luck of the draw, not get good representation of subgroups in a population. To deal with these issues, we have to turn to other sampling methods.
Stratified Random Sampling
Stratified Random Sampling, also sometimes called proportional or quota random sampling, involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup. In more formal terms:
Objective: Divide the population into non-overlapping groups (i.e., strata) N1, N2, N3, ... Ni, such that N1 + N2 + N3 + ... + Ni = N. Then do a simple random sample of f = n/N in each strata.
First, SRS assures that you will be able to represent not only the overall population, but also key subgroups of the population, especially small minority groups. If you want to be able to talk about subgroups, this may be the only way to effectively assure you'll be able to. If the subgroup is extremely small, you can use different sampling fractions (f) within the different strata to randomly over-sample the small group (although you'll then have to weight the within-group estimates using the sampling fraction whenever you want overall population estimates). When we use the same sampling fraction within strata we are conducting proportionate stratified random sampling. When we use different sampling fractions in the strata, we call this disproportionate stratified random sampling.
Second, SRS will generally have more statistical precision than simple random sampling. This will only be true if the strata or groups are homogeneous. If they are, we expect that the variability within-groups is lower than the variability for the population as a whole. Stratified sampling capitalizes on that fact.
Because the groups are more homogeneous within-group than across the population as a whole, we can expect greater statistical precision (less variance). And, because we stratified, we know we will have enough cases from each group to make meaningful subgroup inferences.
Systematic Random Sampling
Here are the steps you need to follow in order to achieve a systematic random sample:
number the units in the population from 1 to N
decide on the n (sample size) that you want or need
k = N/n = the interval size
randomly select an integer between 1 to k
then take every kth unit
Cluster (Area) Random Sampling
The problem with random sampling methods when we have to sample a population that's disbursed across a wide geographic region is that you will have to cover a lot of ground geographically in order to get to each of the units you sampled.
In cluster sampling, we follow these steps:
divide population into clusters (usually along geographic boundaries)
randomly sample clusters
measure all units within sampled clusters
Multi-Stage Sampling
The four methods we've covered so far -- simple, stratified, systematic and cluster -- are the simplest random sampling strategies. In most real applied social research, we would use sampling methods that are considerably more complex than these simple variations. 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. When we combine sampling methods, we call this multi-stage sampling.

Values and Lifestyles (VALS)
www.sric-bi.com
VALS™ is a marketing and consulting tool that helps businesses worldwide develop and execute more effective strategies. The system identifies current and future opportunities by segmenting the consumer marketplace on the basis of the personality traits that drive consumer behavior. VALS applies in all phases of the marketing process, from new-product development and entry-stage targeting to communications strategy and advertising.The basic tenet of VALS is that people express their personalities through their behaviors. VALS specifically defines consumer segments on the basis of those personality traits that affect behavior in the marketplace. Rather than looking at what people do and segregating people with like activities, VALS uses psychology to segment people according to their distinct personality traits. The personality traits are the motivation—the cause. Buying behavior becomes the effect—the observable, external behavior prompted by an internal driver.

PRIZM
http://www.claritas.com
Claritas PRIZM NE (The New Evolution) combines the demographic and consumer behavior segmentation data needed to easily identify, understand and target customers and prospects. PRIZM NE defines every household in terms of 66 demographically and behaviorally distinct types, or "segments," to help learn about their likes, dislikes, lifestyles and purchase behavior. With the capabilities of consumer behavior segmentation information, businesses can better understand customers and prospects and to target them more efficiently with tailored messages and products designed just for them.
With PRIZM marketing analysis, household and neighborhood-level segment assignments are linked to provide a seamless transition from market planning and media strategy, to customer acquisition, cross-selling and retention.
Claritas pioneered customer segmentation systems for commercial use over 30 years ago – to provide marketers with an insider's view of their customers and prospects. Claritas PRIZM, the first segmentation system of its kind, provides a standard way of sorting the population into similar groups by demographics, lifestyle preferences and behaviors to provide actionable target marketing information.