What is Confidence Level and Sampling Error?

In survey research, the people who respond to the survey are a sample of the population. The goal is to obtain a representative sample to be certain that the people who respond to the survey represent the thinking of the entire population, as if you had a 100% response rate. Confidence level tells you how…

What are the Different Types of Validity That Must be Considered When Conducting a Survey?

The different types of validity that are important to survey research include construct validity, convergent validity, content validity, representation validity, face validity, criterion validity, concurrent validity, predictive validity, statistical conclusion validity, internal validity, external validity, and ecological validity. Descriptions of each are provided below. Construct validity refers to the extent to which a survey measures…

What is Validity and Why is it Important for Survey Results?

There are many different types of Survey Validity, such as construct validity, convergent validity, and content validity. Validity is important because it determines what survey questions to use, and helps ensure that researchers are using questions that truly measure the issues of importance. The validity of a survey is considered to be the degree to…

What is a SWOT analysis?

The normative data scale ranges from 1 to 100. This scale is divided into four quartiles:  Strengths, Opportunities, Weaknesses, and Threats. The SWOT Analysis is performed at the Topic level and enables you to immediately understand how the various topics are performing. The categories are further defined below: The Strength Category includes topics with normative…

What Type of Survey Data Analysis does NBRI Provide?

ClearPath Analytics convert your survey data into actionable information. Through correlations, regressions, and path analyses, NBRI identifies the key drivers of employee engagement and customer loyalty.

What Type of Statistical Analysis is Needed for Your Survey?

Different types of data require different types of statistical analyses. This is especially true of psychological research, including employee surveys, customer surveys, and market research. For instance, a stand-alone employee survey is reported in terms of its N’s, means, distribution of responses, and benchmarking scores, but it may be analyzed with a Root Cause Analysis,…

What is a Root Cause Analysis of Survey Data?

As meaningful, important, and necessary as comparisons with benchmarking data are in order to understand survey results, management is still left with the subjective task of prioritizing results, and deciding which survey items should be addressed. Variations in manpower and material requirements that are needed to intervene into problem areas have to be considered, and…

How Should I Analyze Qualitative Survey Data?

Qualitative data is information that is in language form. In other words, it is the survey data that is obtained in response to open-ended questions. In an ideal world, all qualitative survey data should be coded according to the following steps. First, at least one experienced coder should read all the responses to a specific…

What is Quantitative Survey Data?

Quantitative survey data has a number associated with it. Quantitative data can be as simple as the percent of people responding “Yes” or “No” to a specific question. However, one of the most valuable uses of quantitative survey data comes from questions that are scaled. A scaled question is a question that requires respondents to…

Should I Look at Employee Survey Results by Generation?

Employees within different generations feel differently about feedback, time off, benefits, and management interaction. Communication styles may also differ from one generation to another. And, you’ll find that some generations are more receptive to teamwork and function better in a collaborative environment. Understanding these differences can enable you to effectively address issues, leverage strengths, and…