Tag: McQuarrie

Calculating Sample Sizes

The market research toolboxThroughout this week’s lectures in our Marketing Research & Strategy class, we’ve been taking a closer look at how to determine a sample size for a research project. McQuarrie (2016) provides a relatively easy way to calculate how big your sample size should be in his book The Market Research Toolbox: A Concise Guide for Beginners. In his book, he describes a three-step process that will help you calculate the sample size based on the preferred confidence level and margin of error (precision) that is proposed by management judgement:

  1. Square the Z value associated with the desired confidence interval.
  2. Multiply it by the population variance.
  3. Divide by the square of the desired precision.

To find the population variance, you have to use the following formula:

Variance = proportion #1 x [1 – proportion #1]

sample size

Now we know how to theoretically calculate the sample size, we can apply this to a problem. One of the problems that was presented by McQuarrie (2016) stated: “To determine the effectiveness of an ad campaign for a new DVD player, management would like to know what percentage of the market has been made aware of the new product. The ad agency thinks this figure could be as high as 70 percent. In estimating the percent aware, management has specified a 95 percent confidence interval, and a precision of ±2 percent. What sample size is needed?”

Following the method presented by McQuarrie for calculating the sample size, the first thing we need to do is to square the Z value associated with the confidence interval. The problem states that management decided on a confidence interval of 95 percent which means that our Z value equals 2. In the next step, we have to multiply our squared Z value with the population variance, which can be calculated through the formula shown above for variance. In this case the variance equals 0.21 [0.70 x (1-0.70)]. Once, we’ve established this, we have to divide our nominator (Z2 x variance) through our denominator, which equals the square of the desired precision. This means that our final formula will look like this:

[22 x 0.21] / 0.022
= 2100

I think a margin of error (precision) of ±2 percent is a reasonable confidence interval. At first, it seemed really tight but after taking a closer look at an article by Billy Hulkower, a Senior Technology Analyst for Mintel, on the market for Movie Sales and Rentals in the US in 2014, we can conclude that the market for movie sales and rentals is declining rapidly. The tables show that movie sales in the US, when adjusted for inflation will decline from $17.5 billion in 2014 to $14.6 in 2019. Based on this information, it is extremely important for a company, that is about to introduce a new DVD player into the market, to know how effective their ad campaign will be.

Another reason why it is so important for companies in the DVD player market to know how effective their ad campaign will be, is the increasing competition of digital movies provided by for example Amazon Instant Video, iTunes, and Google Play which is also discussed in Hulkower’s article. Technology is constantly evolving and helps us make our lives easier. Customers now have the option between buying movies at home from their computers or running to the store to physically buy the movie. I think we can all agree that it is a lot more attractive to stay at home and buy a movie online without having to leave your couch instead of driving all the way to the store for that same movie.

Due to these two reasons, a ±2 percent precision level in this problem seems a very reasonable estimate, because as a company in a declining market with a lot of competition wants to get an accurate reflection of the percentage of the market that is aware of the new DVD player that you are to introduce into the market.

Surveys as part of quantitative research

Survey

The last couple of lectures in our Marketing Research and Strategy class, we have been taking a closer look at quantitative research. More in particular, the types of surveys and how they are set up. One of the differences between surveys, discussed by McQuarrie (2016), who is a professor at Santa Clara University, is that some surveys are set up to describe customer characteristics and behaviors, while other describe a customer’s stance toward the brand, or positive and negative experiences with product ownership. Describing customer characteristics and behaviors is referred to as descriptive surveys. When describing a customer’s stance toward the brand, or positive and negative experiences with product ownership, then we’re talking about evaluative surveys. McQuarrieEdward McQuarrie (2016) states that the purpose of descriptive surveys is limited compared to evaluative surveys, because they have a greater claim on your research dollar. He says that it is more important to know if the customer’s satisfaction is dropping or that dissatisfaction is pared to a particular action on the company’s part. Also, a company will want to know if its brand is fading in customer perceptions or if a competitor’s brand is gaining strength. What I think McQuarrie means with this is that he thinks it is more important to find out first why something is happening, before finding out how customers are feeling towards the product or service. He says that it is more important to find out the direct cause of why something is happening first, before continuing to find out the underlying reasons.

As a student at WVWC, I’ve taken course evaluation surveys at the end of every semester. These surveys are a prime example of an evaluative survey, because through these surveys professors try to find out if what they are doing is directly helping students. These surveys are set up in two different sections. The first section of the survey looks at how the student perceived the course. These questions are looking for more information about if the course objectives were met, if the professor presented the material across well, etc.
On the other hand, the second section of the survey looks more at the individual professor and their performance in teaching the course. Here the questions are looking more at if students felt comfortable asking questions, if assignments were returned within reasonable time, if students received the full attention of the professor when asking questions, etc.

Comparing my personal experience of taking surveys with the description of descriptive and evaluative surveys given by McQuarrie, we can take two different perspectives. First, we can look at it from the student’s perspective. Students might value a survey like the course evaluations as unimportant. One of the reasons for this is that they are completing a survey for a course that they will most likely never have to take again (as long as the don’t fail the class). Another reason is that there isn’t an incentive for students to complete this survey. In the short run, they are not getting anything out of it. The only way taking the survey will help the student is if he or she has that same professor again for another course throughout his or her college/university career.
On the other hand, we can look at it from the marketer conducting the survey (the college or university in the case of course evaluations). In their case, the course evaluations are extremely important. Based on these surveys, they try to increase the quality of the professors teaching the specific courses, which would result in a higher quality of education for the college or the university. Considering that students take these surveys truthfully, the college or university and its professors will make important decisions for the future of that course and the education of future students.