What Do My Results Really Mean?

How do you uncover patterns and trends in the mountains of data your organisation accumulates? Today, more than ever, advanced analytical methods, such as predictive modeling and segmentation, are the secret weapons of many successful businesses.

With advanced analytical methods you can answer the questions that make a difference. Performance score reporting can answer, “Which region has the highest levels of employee commitment?” however advanced analytical methods can go deeper and answer the question “How has this region achieved these high levels of commitment?” Answering the question “how” tells you which factors caused the region’s high levels of commitment, empowering you to make changes in other areas, thereby, increasing your organisation’s competitiveness.

Therefore, in addition to the ‘slice and dice’ analysis possible using the standard reporting options available in www.survey-online.com, further advanced analyses using the statistical package SPSS can provide valuable insight into the following questions (and others like them):

• How important are the variables we measure?
• What are the key drivers of employee satisfaction and other critical outcomes?
• What significant differences exist across demographic groups within the organisation?
• Who are our ‘at risk’ employees?
• Where should we be focusing our efforts to gain the greatest leverage to performance improvement?

These are all the types of questions that face decision-makers once they receive their survey results. One approach to finding the answers involves wading through pages of data, cross-tab after cross-tab, etc. This approach is time consuming, expensive and not entirely reliable. On the other hand, SPSS analysis and the application of applied statistical techniques can uncover the drivers and points of difference, providing decision-makers with clear direction for focus.

We use a variety of Multivariate analysis techniques, including, but not limited to:

• Regression analysis
• Significance testing (including ANOVA, Chi-square, and T-tests)
• Factor analysis
• Structural Equation modeling