Research Methodology

Lifestory Research conducts the America’s Most Trusted® study. Established as a line of research in 2012, the study stresses the vital influence of consumer trust in brand evaluations and purchase decisions. The core index used to evaluate and compare brands is the Net Trust Quotient Score.

America’s Most Trusted Study tracks brands that provide services or products to customers in their home. The brands included in the study are among the largest in the residential new home industry. The goal of this study is not to include all brands. As a result, many brands are excluded from the study.

Survey Methodology

America’s Most Trusted® surveys are conducted throughout the course of the year. The goal of administering the survey over the course of a 12 month period is to reduce any historical artifacts or bias in events occurring in a specific time period. The survey is designed to address the nuances of each brand category included in the study. Brands that serve customers across the country, as well as brands that serve customers within a specific geography, are administered to match these brand conditions.

The America’s Most Trusted® study is a multifaceted survey instrument in which all participants complete certain sections of the survey, as well as a specific battery of questions that are only completed by qualified participants. Qualification to complete a section of the survey is based on participants indicating that they are actively considering or shopping for a specific product. For example, those who indicate that they are actively shopping for a home are asked to complete the applicable sections of the survey that relate to this sub-sample characteristic. Moreover, a specific battery of brand category questions is randomized to participants to manage the length of survey objectives in the study.

Participants in the study are also qualified for brand awareness. Participants are asked to identify brands they have seen or heard of before (i.e., brand awareness). (It should be noted, we make every effort to include all qualified brands, however, some brands may not be included in the study). If a participant indicates they are aware of the brand, they are then asked to evaluate it along a set of attributes, including trust. If a person indicates they are unaware of a brand, they do not complete any subsequent questions about that brand. In short, brand awareness is a requirement in order for people to complete questions about a brand. A brand is included in the trust rankings only if the brand awareness is significant enough to generate a reasonable sample size. Given this criterion, some brands not reported in the rankings were excluded because the brand did not garner a high enough brand awareness rating.

Sampling

The study uses a non-probability sample design, recruiting participants via online panels. The advantages and limitations of online panels regarding the representation of the sample to the population have received much attention from research professionals. While this debate is ongoing, the tide has begun to change in recent years, and more organizations are recognizing the value and quality of online research samples. The inclusion of a margin of error is typically restricted to probability samples. However, given the preponderance of online studies noting the margin of error, here we outline the nature of the margin of error and note the margin of error computed for this study.

To better understand the notion of sampling error, it is helpful to recall that data from a sample provides merely an estimate of the true proportion of the population that has a particular characteristic. If 100 different samples are drawn from the same sampling frame, they could potentially result in 100 different patterns of responses to the same question. These patterns, however, would converge around the true pattern in the population.

The sampling error is a number that describes the precision of an estimate from any one of those samples. It is usually expressed as a margin of error associated with a statistical confidence level. For example, a presidential preference poll may report that the incumbent is favored by 51% of the voters, with a margin of error of plus or minus 3 points at a confidence level of 95%. This means that if the same survey were conducted with 100 different samples of voters, 95 out of the 100 different samples (95% confidence level), it would be expected to show the incumbent favored by between 48% and 54% of the voters (51% ± 3%).

In this study, the overall margin of error (confidence interval) at a 95% confidence level is ± 2.00% and is ± 3.00% at most within any brand category. Namely, given that several thousand responses are collected within each brand category, there is a small margin of error in the overall study and the brand-specific category. This means that any results we find in this study are likely to be the responses generated if the same study were performed multiple times. With that said, it is important to understand that the margin of error changes based on the level of analysis you are performing. In this case, when the sample size is constrained because only a certain number of people in the sample population fit into specific criteria, the margin of error adjusts as well.

Index Score Calculation

To assess the Net Trust Quotient Score, consumers are asked to evaluate how much they trust each of the brands in the study. Consumers are asked several questions about the brand including their impression of the trustworthiness of the brands. Net Trust Quotient Scores are calculated based on how a consumer evaluated a specific brand on the trust questions and scales used. Scores are divided into three categories: “advocates,” customers who feel a significant trust toward the brand; “neutrals,” those who trust a specific brand, but do not see a specific brand as standing on the shoulders of other brands; and “antagonists,” who are skeptics with little, if any, trust in a specific brand.

To compute an index score, the results from the scale responses are converted into a z score. A z score is a common statistical way of standardizing data on one scale so a comparison can take place is using a z-score. Each z-score corresponds to a point in a normal distribution and as such is sometimes called a normal deviate since a z-score will describe how much a point deviates from a mean or specification point.

A z-score (aka, a standard score) indicates how many standard deviations an element is from the mean. A z-score can be calculated from the following formula: z = (X – μ) / σ, where z is the z-score, X is the value of the element, μ is the population mean, and σ is the standard deviation.

Once the z scores are identified, these scores are converted to T scores. T scores are used to inform individuals how far their score is from the mean. An advantage of using a T score over a z score is that T scores are relatively easy to understand and compare across each brand in the study.

In this study, T scores have a mean of 100 and a standard deviation of 10. All brand T scores, across all categories, are based on all brands in the study.