[et_pb_section fb_built=”1″ fullwidth=”on” _builder_version=”4.19.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_fullwidth_post_title author=”off” categories=”off” comments=”off” featured_placement=”background” _builder_version=”4.19.4″ _module_preset=”default” title_font=”montserrat-variablefont_wght|700|||||||” title_text_color=”#FFFFFF” title_font_size=”30px” title_line_height=”1.5em” meta_font=”montserrat-variablefont_wght||||||||” meta_text_color=”#FFFFFF” use_background_color_gradient=”on” background_color_gradient_direction=”90deg” background_color_gradient_stops=”rgba(0,127,127,0.96) 0%|rgba(5,186,176,0.91) 100%” background_color_gradient_overlays_image=”on” custom_padding=”175px||234px|||” custom_padding_tablet=”100px||200px||false|false” custom_padding_phone=”50px||||false|false” custom_padding_last_edited=”on|phone” title_font_size_tablet=”30px” title_font_size_phone=”20px” title_font_size_last_edited=”on|phone” global_colors_info=”{%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22background_color_gradient_stops%22%93}”][\/et_pb_fullwidth_post_title][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”off|desktop” admin_label=”Header” _builder_version=”4.19.4″ background_enable_image=”off” width=”100%” max_width=”1080%” module_alignment=”center” custom_padding_tablet=”||||false|false” custom_padding_phone=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.19.4″ _module_preset=”default” custom_margin=”-200px||||false|false” custom_padding=”0px||||false|false” border_width_all=”6px” border_color_all=”#FFFFFF” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.19.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.edl-consulting.com\/wp-content\/uploads\/2023\/03\/15698563.png” title_text=”15698563″ _builder_version=”4.19.5″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text quote_border_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ _builder_version=”4.19.5″ _module_preset=”default” text_line_height=”1.8em” quote_font=”montserrat-variablefont_wght||||||||” quote_text_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ quote_font_size=”20px” header_3_font=”montserrat-variablefont_wght||||||||” header_3_text_color=”gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962″ header_3_font_size=”16px” global_colors_info=”{%22gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962%22:%91%22header_3_text_color%22%93,%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22quote_text_color%22,%22quote_border_color%22%93}”]<\/p>\n
Employee surveys offer enormous potential for insights. They can help to detect problems, predict and prevent turnover intentions, improve employee well-being and job satisfaction or refine processes. In our previous blog post (LINK), we clarified that a clean survey setup is crucial for successful data collection. But how can the right conclusions be drawn from the answers given?<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.19.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.19.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.19.5″ _module_preset=”default” text_font=”montserrat-variablefont_wght||||||||” text_line_height=”1.89em” header_2_font=”montserrat-variablefont_wght||||||||” header_2_text_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ header_3_font=”montserrat-variablefont_wght||||||||” header_3_text_color=”gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962″ header_3_font_size=”16px” global_colors_info=”{%22gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962%22:%91%22header_3_text_color%22%93,%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22header_2_text_color%22%93}”]<\/p>\n Once the answers of a survey are collected, they need to be analyzed and set into context. With the right methods, the thought patterns and opinions of your employees can be identified. In turn, you can use the information gained to solve problems and optimize processes.<\/span><\/p>\n The following steps will help you get the most out of your survey results:<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.19.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.19.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.19.5″ _module_preset=”default” text_font=”montserrat-variablefont_wght||||||||” text_line_height=”1.89em” header_2_font=”montserrat-variablefont_wght||||||||” header_2_text_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ header_3_font=”montserrat-variablefont_wght||||||||” header_3_text_color=”gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962″ header_3_font_size=”16px” global_colors_info=”{%22gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962%22:%91%22header_3_text_color%22%93,%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22header_2_text_color%22%93}”]<\/p>\n Before starting the analysis of the survey, the gathered data should be prepared carefully. This guarantees a smooth procedure and improves the efficiency of the analysis. Two questions are crucial to be answered before starting the analysis.<\/span><\/p>\n What do we want to measure with our questions?<\/span><\/i><\/p>\n Which questions measure the same variable?<\/span><\/i><\/p>\n Broadly speaking, there are different types of data analysis that can be used to retrieve information from the survey. Although these overlap and connect to each other, they can be separated into Descriptive Analysis, Diagnostic Analysis, and Prescriptive Analysis.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.19.5″ _module_preset=”default” text_font=”montserrat-variablefont_wght||||||||” text_line_height=”1.89em” header_2_font=”montserrat-variablefont_wght||||||||” header_2_text_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ header_3_font=”montserrat-variablefont_wght||||||||” header_3_text_color=”gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962″ header_3_font_size=”16px” global_colors_info=”{%22gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962%22:%91%22header_3_text_color%22%93,%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22header_2_text_color%22%93}”]<\/p>\n The simplest way to start analyzing your data is to create descriptive statistics. They summarize information such as gender, age, education, or years employed. They also provide an overview of the variables that were measured.<\/span><\/p>\n <\/p>\n The descriptive analysis can also be used to understand which variables are especially high or low in certain groups. Let\u2019s assume that the goal of an employee survey is to understand the job satisfaction of your employees. The descriptive analysis summarizes the score of job satisfaction based on factors such as gender, age group, or department.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.19.5″ _module_preset=”default” text_font=”montserrat-variablefont_wght||||||||” text_line_height=”1.89em” header_2_font=”montserrat-variablefont_wght||||||||” header_2_text_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ header_3_font=”montserrat-variablefont_wght||||||||” header_3_text_color=”gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962″ header_3_font_size=”16px” global_colors_info=”{%22gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962%22:%91%22header_3_text_color%22%93,%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22header_2_text_color%22%93}”]<\/p>\n The second step is a diagnostic analysis which answers the question of \u201cWhat happened and why did it happen?\u201d. Suppose the descriptive analysis showed a surprising result, for example, significantly lower job satisfaction in the sales department than in the finance department. In that case, it is now time to examine the reasons for this deviation. This step can often be done by observing additional variables and analyzing how they correlate. For example, it may be examined that the sales department has a lower team cohesion, which is the reason for the lower job satisfaction.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.19.5″ _module_preset=”default” text_font=”montserrat-variablefont_wght||||||||” text_line_height=”1.89em” header_2_font=”montserrat-variablefont_wght||||||||” header_2_text_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ header_3_font=”montserrat-variablefont_wght||||||||” header_3_text_color=”gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962″ header_3_font_size=”16px” global_colors_info=”{%22gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962%22:%91%22header_3_text_color%22%93,%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22header_2_text_color%22%93}”]<\/p>\n In the last step, the newly gained information is used to determine a course of action. In this process, solutions are sought for the problems uncovered.<\/span><\/p>\n <\/p>\n If the previous analysis showed that job satisfaction in the sales department is lower due to less team cohesion, one solution may be to integrate regular team-building measures.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.19.5″ _module_preset=”default” text_font=”montserrat-variablefont_wght||||||||” text_line_height=”1.89em” header_2_font=”montserrat-variablefont_wght||||||||” header_2_text_color=”gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154″ header_3_font=”montserrat-variablefont_wght||||||||” header_3_text_color=”gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962″ header_3_font_size=”16px” global_colors_info=”{%22gcid-f7ac8586-8e78-4ff7-ba3b-b014d456e962%22:%91%22header_3_text_color%22%93,%22gcid-2784e1ce-2768-4fd5-82f8-c8810b7e6154%22:%91%22header_2_text_color%22%93}”]<\/p>\n1. Preparing your data<\/h2>\n
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2. Descriptive Analysis – What happened?<\/h2>\n
3. Diagnostic Analysis – Why did it happen?<\/h2>\n
4. Prescriptive Analysis – What do we do now?<\/h2>\n
5. Open questions – Providing context to the results<\/h2>\n