Use this sample of employee satisfaction survey results analysis to effectively analyze the survey results.
While it is crucial to perform employee satisfaction survey, the exercise will be unfruitful if the results are not analyzed. Whether you are using open-ended or closed questions, the results will not make sense if not interpreted critically. How do you perform the employee satisfaction survey results analysis? Let’s look at the process together.
Compiling and reviewing
As mentioned earlier, most of the data you will get will be coded and mixed up. The first step to conducting a comprehensive analysis is compiling and reviewing the data. Categorize the data based on the respondents or the departments. At this juncture, focus on the response rate. If it is low, you might have to repeat the survey.
Clean the data
Note that this does not mean you should doctor the results. Instead, you should edit the data to remove any mistakes such as double entries of information by the same respondent. This will ensure that the final analysis results are accurate and unbiased.
After you have compiled and edited the data, start analyzing. Here you can choose various methods of analysis depending on the objectives of the employee satisfaction survey and/or the complexity of the data. They include:
If you have not carried out other surveys, you could use the results of the present survey as the benchmark for others. In the subsequent surveys, you will be able to relate the findings to the standards you have set.
Trending is another analytical tool used by analysts. For example, if in the last year the number of respondents who were satisfied with a certain training program at the workplace was 75% and you find that this year the number was 80%, you could conclude that there has been an increasing trend.
- Comparative analysis
You can compare the data sets from various departments. For example, you can compare the percentage of employees in the sales department and those from the administration unit, who demonstrated their satisfaction with regard to the working conditions.
However, benchmarking, comparative, and trend analysis may not explain why the data relates, increases, or drops. To deduce the aforementioned relationships, you will have to employ other techniques for analysis including:
- Regression analysis
This type of analysis allows you to find the relationship between two or more factors. For example, you might want to find why 80% of the participants responded they were satisfied with a particular training program. Some of the reasons provided include the teaching plan, method of instruction, the accrued benefits upon completion of the training, and much more. All these factors will not come out clearly when you use benchmarking or comparative analysis. However, you can make reliable conclusions with regression analysis.