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Analyzing Survey Data
Survey data is usually analyzed in several stages using a variety of statistical procedures.  Generally, they fall into one of three broad categories.
Descriptive Statistics:  There are a wide range of statistics that are used to describe or summarize data.  Most tables in survey reports present descriptive statistics for one or more groups or sub-groups among the respondents.  For surveys, these usually are limited to either the the various percentages of respondents who select from a range of categorical alternatives (e.g., "Very Dissatisfied" to "Very Satisfied", women v. men, position in the company, etc.) or as averages (mean) or median (and percentile) scores on items.  Descriptive statistics computed for sample data (survey respondents) are used as estimates for the target population (e.g., all customers or employees) and as such, they should be accompanied by estimates of the margin of error or of the confidence interval  that give some indication that is useful for gauging how accurate these descriptions are.

Tests for Significant Differences:  Usually, tests for significant differences are conducted to examine changes that occur over time or differences between groups or subgroups.  Which specific procedures are used will depend on the type of measurement scale used for the survey items under scrutiny.  Differences in categorical data, for example, may be analyzed using a procedure called Chi2 and differences in average (mean) scores may be analyzed using procedures such as the t-test or the analysis of variance.  By consensus, differences that are unlikely to be the result of chance fluctuations in the data alone (defined as a less than 0.05 probability) are referred to as statistically significant.  Alternatively, you can also examine margins of error or confidence intervals for two groups or subgroups; if they do not overlap, the differences are likely to be statistically significant using other tests.

Statistical Modeling:  There are a variety of procedures from simple correlations between items to more elaborate statistical techniques that allow researchers to evaluate how items relate to other items, individually or jointly.  Regression analysis and more complex statistical modeling procedures are based on the intercorrelations among items and can be used to identify clusters of items that fall together into single dimensions (e.g., which items appear to reflect loyalty?) and to relate these items or dimensions to desired outcomes (e.g., how do satisfaction with compensation, opportunities for advancement, and the effectiveness of organizational communications and other factors relate to employee loyalty and commitment?).  If you have a large enough sample (generally a few hundred or more respondents) these techniques can help you to develop a useful model of how your business objectives can be achieved. 
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