IMC 611 Week 2

RUNNING HEAD: Data Analysis Plan for Trout Unlimited pp. 1

Data Analysis Plan for Trout Unlimited

Stephanie Marchant

West Virginia University


Validation verifies that the survey was conducted as intended. It is especially important

in a third party administered survey, such as the telephone survey that was conducted for Trout

Unlimited (TU). Editing goes hand-in-hand with validation, and involves checking for errors

made by the interviewer (if applicable) and respondents. In TU’s case, the survey was conducted

by a third party, which means there is a possibility of interviewer mistakes. Additionally, there

could have been mistakes made by the respondents because no representative from TU was

present to make any necessary clarifications.

A few questions in TU’s validation process would answer are whether or not correct

demographic data was provided, whether or not answers were consistent, or whether or not the

survey was completed in its entirety. TU’s editing process would check for incomplete answers

to open-ended questions, and whether or not skip patterns were followed.

Coding is the process of marking all the data that fits with particular themes. This

process enables participants to pick examples from the data that will best illustrate the story. As

participants code the data, they continually refine, change, or add to the categories. (Smith &

Albaum, 2013) Coding is a process that continues data analysis, such as:

Develop categories;

Code all the data;

Sort the data;

See what’s unaccounted for; and

Refine the analysis.

Because TU conducted a telephone survey, the need for manual data entry and coding is

required for closed-ended questions. The responses for open-ended questions will also still need

to be coded. Before TU can analyze the data it has collected, the results from the surveys must


be checked once more for errors. To complete this step, SAS (Statistical Analysis System) or

SPSS (Statistical Package for the Social Sciences) software is recommended. (Smith & Albaum,

2013)

Differences analysis is a type of statistical analysis that would be used to compare two

groups of female TU members and determine differences. Differences analysis will be beneficial

in measuring the levels of brand awareness and perceptions between enthusiastic female

members and those not planning to renew. Associative analysis, which examines the

relationship between two variables, would also be useful for TU. (Kiel, 1983) They would be

able to determine if free memberships, demographics, or another factor is related to the

continued support of female TU members. If, for example, TU finds that how female members

are received in their local chapters plays the largest role in influencing membership renewals,

they could conceivably determine that creating female chapters would maintain - and even

encourage - continued membership in the organization.

Multiple regression analysis allows researchers to create predictive models to explain a

behavior or a dependent variable. (Kiel, 1983) Because TU is trying to encourage female

memberships, this would explain the purchase behavior (in this instance, joining TU, is the

dependent variable) by examining which factors, such as chapter involvement, interest in fly

fishing, household income, age and marital status, have the most impact on a woman’s decision

to join and continue to renew their membership.

Discriminant analysis can be used to further examine differences between enthusiastic

female members and those who have no plans to renew. (Sage Publications, 2014) For current

members, it would be interesting to determine if they view the national organization of TU in a

positive light compared to their local chapter. For example, if a respondent indicates they joined


because of the environmental causes that TU supports or because of the free membership? Or,

did they join because they enjoy fly fishing and hoped to find similar-minded individuals with

whom to socialize?

In most research studies, the analysis section follows these three phases of analysis.

Descriptions of how the data were prepared tend to be brief and to focus on only the more unique

aspects to the study, such as specific data transformations that are performed. (Smith & Albaum,

2013) The descriptive statistics can be voluminous. In most write-ups, these are carefully

selected and organized into summary tables and graphs that only show the most relevant or

important information. Usually, the researcher links each of the inferential analyses to specific

research questions or hypotheses that were raised in the introduction, or notes any models that

were tested that emerged as part of the analysis. (McDaniel & Gates, 2013)

In most analysis write-ups it's especially critical to not "miss the forest for the trees." If

too much detail is presented, TU may not be able to follow the central story line of the results.


References

Kiel, G. (1983). Maximizing marketing research effectiveness through data analysis. Journal of

Marketing Practice: Applied Marketing Science, pp. 40-50.

McDaniel, C., & Gates, R. (2013). Marketing research essentials (8th ed.). New York: Wiley &

Sons.

Sage Publications. (2014, July 12). Planning and preparing the analysis. Retrieved from Sage

Publications: http://www.sagepub.com/upm-data/44135_2.pdf

Smith, S. M., & Albaum, G. S. (2013). Basic marketing research. Provo: Qualtrics Labs, Inc.

IMC 611 Week 2

Marketing

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