Advanced Statistical Analysis

Advanced statistical analysis refers to the use of sophisticated statistical techniques and methods to analyze data and draw meaningful insights.

Laptop screen showing graphics of advanced statistical analysis

Market research and surveys are primary business tools in our data-driven world. They’re used to explore market opportunities, gather insights into consumer behavior, and provide exceptional value to the customer experience. Advanced statistical analysis is the foundation of collecting meaningful data for business intelligence.

At Slice MR, we offer market research services that are essential to your business’s success. Our team of highly skilled professionals makes use of advanced reporting and analysis services that are second to none in the industry.

From survey data collection to providing live report links and raw data files, you’ll have maximum accessibility to key findings in research reports and statistical analyses. Keep reading to discover how our team can help your business utilize data-driven decision-making and statistical analysis to meet your goals.

Statistical Analysis Software

SPSS Statistics

Statistical Package for the Social Sciences (SPSS) statistics software is used by researchers for data management, advanced analytics, business intelligence, and more. It’s an exciting research tool that empowers users to unravel complex data sets and uncover meaningful insights.

It offers a wide range of statistical analysis methods, including factor analysis, regression analysis, ANOVA, and cluster analysis, making it a versatile program for data exploration. With SPSS stats, users can dive deep into their data, performing descriptive statistics such as calculating standard deviation, quartiles, and summary statistics.


Now that you’re aware of some of the capabilities of statistical analysis, you’ll be glad to find that there’s more than just one tool you can utilize. Q is advanced software designed to streamline the process of conducting thorough statistical analysis. Researchers can utilize these tools to easily analyze complex data and derive valuable insights. Q supports cluster analysis, analysis of variance (ANOVA), hypothesis tests, and other methods. The software itself is intuitive and allows businesses to visualize information with seamless Microsoft Office and Display integration.

This software fully supports R language, giving users the flexibility to do whatever computations they wish. Researchers can also automate updated reports whenever new data is collected. Thanks to its user-friendliness, creating and manipulating variables is easy.


Quantum is a data platform for advanced statistical analysis that allows researchers to store, enrich, protect, and archive unstructured data. The tool helps unlock the value of data that can be used to drive a business forward. Researchers can use AI to tag, catalog, and index data, making it easier to find, recall, and reuse.

Our team at Slice MR can leverage this platform to help your business utilize data to innovate new products, inform decisions, and overall improve lives.

Advanced Statistical Analysis Techniques

The software tools used above offer a variety of capabilities and techniques. Check out some of the best statistical methods to use in order to conduct effective market research and ensure reliable data collection.

Cluster Analysis

Cluster analysis enables analysts to uncover hidden gyms within a dataset. This technique groups similar data points together, creating distinct clusters that showcase their unique characteristics. Imagine the thrill of discovering clusters of customer preferences or identifying segments within a massive dataset. It’s like cracking a code or solving a complex puzzle, only with numbers.

Cluster analysis goes beyond mere categorization; it allows you to delve deeper into the relationships between data points. This powerful method helps you identify similarities and disparities, enabling a comprehensive understanding of the underlying structure of your data.

Multivariate Regression

Multivariate regression is an exciting feature of advanced statistical analysis software that allows researchers to explore complex relationships between multiple variables. Researchers can examine the interconnections between several predictors and a single outcome.

This technique employs a mathematical equation, known as a regression equation, to model the relationship between the predictors and the outcome. It calculates the coefficients for each predictor, indicating their relative impact on the outcome variable. The regression equation allows us to make predictions based on the values of the predictors.

Significance Testing (Z-Test or Chi Square)

Significance testing allows researchers to determine whether the results obtained from a statistical analysis are statistically significant or if they occurred by chance. There are several analysis methods available, such as Z-test and Chi Square, that help in conducting this analysis.

  • Z-Test: The Z-test is commonly used when the sample size is large, selected at random, and the population standard deviation is known. It’s particularly effective in hypothesis testing related to means, providing information about whether the observed mean values differ significantly from the hypothesized population mean.
  • Chi Square: The Chi Square test is suitable when the variables being analyzed are categorical, measuring the association between two or more categorical variables and determining if there is a significant relationship between them.

Factor Analysis

Two people going over paperwork showing data after advanced statistical analysis

Factor analysis delves into statistics analysis and data analysis, allowing researchers to identify underlying latent factors that contribute to the observed variables. It helps explain the variance within the data, allowing for a better understanding of the relationships between variables. By reducing the complexity of large data sets, factor analysis simplifies data interpretation and visualization.


In the simplest terms for advanced statistical analysis, segmentation refers to the process of breaking down a larger dataset into smaller, more manageable groups, or segments. These segments are created based on certain shared characteristics, allowing researchers to gather information into the data at hand.

Regression Analysis

By utilizing regression analysis, researchers can investigate the relationships between multiple independent variables and a single dependent variable. This method helps in determining the extent to which independent variables affect the outcome variable, allowing for the identification of significant predictors.

Whether it’s analyzing patient outcomes in the field of anesthesiology or examining the impact of interventions in diverse areas such as genomics or market analysis, regression analysis facilitates groundbreaking research and data-driven decision-making.


Correlation is a statistical measure to denote the association between two quantitative variables. Assuming the association is linear, one variable increases or decreases a fixed amount for a unit increase or decrease in the other.

Customer-Centric Statistical Analysis Techniques

Below, you’ll find analysis techniques and theories involving other types of collected data. It can reveal more about customer sentiment, the psychology behind consumer behavior, and more.

Word Cloud

The word cloud provides a refreshing approach to analyzing and understanding complex datasets. The cloud takes your raw data and transforms it into a captivating visual representation, where the most frequently occurring words appear larger and bolder, instantly catching your eye. Unveil patterns and trends within your dataset with just a glance.

Algorithm Development

In advanced statistical analysis, algorithm development involves the fusion of mathematical concepts and programming skills. Carefully constructing these algorithms allows professionals to unravel complex phenomena and gain valuable information.

From understanding consumer behavior and predicting market trends to making medical diagnoses and optimizing industrial processes, the potential applications are vast. The possibilities are endless and discovering new algorithms that push the boundaries of statistical analysis opens up new avenues for exploration and innovation in data.

Kano Analysis

Office with desks full of professional market researchers using advanced statistical analysis

Kano analysis is a framework developed by Dr. Noriaki Kano in the 1980s that classifies customer requirements into five categories: basic, performance, excitement, indifferent, and reverse. The sheer brilliance lies in its ability to prioritize features based on customer satisfaction and delight.

It delves deeper into the realm of customer emotions, understanding what makes their hearts race and their eyes light up with joy. By identifying these sentiments, businesses can transform their products into true crowd-pleasers that consistently exceed customer expectations.

Discrete Choice and Max-Diff

Are you looking to delve deeper into consumer decision-making and uncover the preferences behind their choices? Look no further than discrete choice analysis and Max-diff (maximum difference scaling) techniques. These powerful research tools allow you to learn more about the choices people make and identify the features or attributes that have the greatest impact on their decision-making.

  • Discrete choice: Also known as “choice modeling,” this is a statistical technique used to understand how individuals make choices when presented with a set of alternatives. It breaks down the decision-making process into discrete, measurable components, enabling researchers to quantify the relative importance of different attributes influencing consumer choices.
  • Max-diff: A survey-based method used to measure the relative importance or preference for a set of attributes or features. It asks respondents to choose the best and worst options from a series of alternatives, thereby forcing them to make trade-offs and reveal their preferences. The max-diff technique is particularly useful when there are numerous attributes to consider and when evaluating how different attributes impact decision-making.

Van Westendorp Pricing

The Van Westendorp Pricing model essentially focuses on capturing customers’ price perception by examining their willingness to pay (WTP). It involves four key concepts: the Point of Marginal Cheapness (PMC), the Point of Marginal Expensiveness (PME), the Indifference Point (IP), and the Optimal Price (OP).

To conduct a Van Westendorp Pricing analysis, you need to survey your target customer base and collect data on their perceived price sensitivity, exploring their willingness to purchase at different price points. By analyzing and plotting the data, you can identify the price range offering the highest potential for profits and sales volume.

Overall, the importance of advanced statistical analysis software and its many capabilities cannot be overstated. This remarkable technology has revolutionized the field of data analysis and decision-making. Altogether, it empowers researchers, businesses, and organizations to uncover valuable information, make data-driven decisions, and stay ahead of the competition.

That’s why our team here at Slice MR utilizes these techniques and tools to help our clients through survey optimization, programming, testing, data collection, and data processing with ease. Request a quote for statistical analysis today.