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Survey Statistical Analysis Methods

Market research 04 12
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Table of Contents

What is Survey Analysis?

Survey analysis, as the name suggests, refers to the process of analyzing survey results from customers, employees, or others. It is used to draw conclusions from the data gathered from surveys. It is a cost-effective method to study customer preferences and behavior as it is often impossible to survey everyone in your target population.

Statistical survey analysis is important as it allows you to draw inferences and generalisations of your whole target audience through the sampling of a small subset of this population. Responses and statistical information alone does not benefit a company. It’s the inferences, trends, and patterns that they identify within these responses that help them make better decisions. 

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Types of Survey Analysis

The following are some popular types of statistical survey analysis methods:

  1. Regression Analysis
  2. ANOVA Test
  3. Conjoint Analysis
  4. The T-Test
  5. Crosstab Analysis
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Stages of Customer Loyalty

Customer Loyalty in some form can be seen as a ladder of commitment for a customer. These stages in the ladder increase the loyalty of a customer as they move up. 

  • Awareness: the stage when customer find out about the brand
  • Research: customer looks up for information regarding the brand and considers purchasing
  • Buy: customer successfully buys for the first time
  • Use: customer uses the product or service for the first time 
  • Repeat: after a positive experience customer comebacks to purchase from the brand
  • Refer: customer refers the brand and its product/service to friends and family
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Regression Analysis

Regression analysis includes a set of statistical methods employed in the estimation of the relationships between a dependent variable with one or more independent variable(s). This method of analysis mathematically sorts out which variables have the most impact, and the way these variables interact with each other. It is a useful method when trying to define the impact of a change in your independent variable. 

Regression analysis is conducted by gathering data on the variables in question. Then, this information is plotted on a graph/chart. Graphs resulting from regression analysis depict a regression curve that reflects the relationship between the variables. 

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ANOVA test, or analysis of variance test, is used in amalgamation with regression studies in order to identify the effect of independent variables on dependent variables. It can compare a range of variable groups simultaneously to see if there are any corrections between them.

There are two main types of ANOVA tests:

  1. One-way ANOVA: Compares means of two or more independent variables to determine whether there is statistical evidence that their associated population means are different.
  2. Two-way ANOVA: Is an extension of the one-way ANOVA test, and it examines the influence of two different independent variables on one continuous dependent variable. 

There are also variations of the ANOVA test, such as MANOVA. MANOVA, or multivariate analysis of variance, is used to compare multivariate sample means. It helps determine differences between independent groups on more than one continuous dependent variable.


Conjoint Analysis

Conjoint analysis is used in market research to determine how people value different attributes of a product or service, in order to help organizations understand customer preferences and cater to their needs and wants more effectively. It is a survey-based statistical technique that is conducted by breaking a product/service down to its attributes, or features, and testing different combinations of these attributes to identify consumer preferences. 

Survey results can then be used to calculate “preference scores”. This score is a numerical value that measures how much each attribute affected the respondent’s choices to purchase the product. These preference scores are used to build simulators that can forecast the market share for a set of different products offered to the market. This simulator can model respondent decisions and is used to identify specific features and pricing levels that help forecast potential demand in the competitive market.


The T-Test

T-Test is a statistical test used to compare the mean of two groups of variables. It is usually used when the data sets come from the same population, follow a normal distribution, and may have unknown variances. It is used as a hypothesis testing tool, which means it allows the testing of assumptions made about certain populations. 

When T-Test is used, the following assumptions are made about the data:

  1. The scale of measurement applied to the data collected follows a continuous or ordinal scale.
  2. The data collected is from randomly selected units of the population, and is representative of the total population; as in simple random sampling.
  3. When the data is plotted, it results in a normal distribution, bell-shaped curve.
  4. Equal, or homogeneous, variance exists when the standard deviations of the samples are (approximately) equal.


Crosstab Analysis

Crosstab analysis, or cross tabulations, involves the use of data tables that display the results of every respondent. It’s a statistical tool used for categorical data and it enables the examining of relationships that may not be apparent when analysing survey responses. Categorical data refers to values that are mutually exclusive to each other. 

Crosstab analysis helps organizations make informed decisions by identifying patterns, correlations, and trends between the study’s parameters. 

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