{"id":1176,"date":"2010-01-28T01:00:03","date_gmt":"2010-01-28T09:00:03","guid":{"rendered":"https:\/\/systematichr.com\/?p=1176"},"modified":"2010-01-18T14:35:07","modified_gmt":"2010-01-18T22:35:07","slug":"survey-design-101-%e2%80%93-part-2-which-questions-should-i-keep-or-drop","status":"publish","type":"post","link":"https:\/\/systematichr.com\/?p=1176","title":{"rendered":"Survey Design 101 \u2013 Part 2: Which questions should I keep or drop?"},"content":{"rendered":"

Guest Author:\u00a0 Stephen B. Jeong, Ph.D.<\/em><\/p>\n

In Part 1 of Survey Design 101, we discussed two broad topics related to survey design \u2013 choosing the right topics and creating quality questions.\u00a0 Survey design (or questionnaire development), however, is not complete until you can show that all or most of the redundant questions have been filtered out from the final set.\u00a0 Moreover, this \u201credundancy\u201d is often only visible through statistical analysis (i.e., factor analysis, discussed below) conducted after the data have been collected.\u00a0 In other words, the initial draft of survey questions needs to be treated as just that \u2013 an initial draft. It\u2019s only after the first data collection and subsequent revision that survey design can be said to be complete.\u00a0 Beyond this, additional data should be used \u2013 collected on an annual or biennial basis \u2013 for continued refinement of the survey questions.<\/p>\n

One common method used to refine survey questions is through factor analysis \u2013 a data reduction technique.<\/p>\n

Factor analysis has been around for nearly a century (see Charles Spearman and intelligence testing); and although the mathematics involved \u2013 linear algebra \u2013 may seem intimidating, the concept is simple \u2013 it\u2019s a technique used to reduce a large number of variables into a smaller set by examining the interrelationships among the variables. Fortunately for most of us, understanding how it can be used to improve the quality of our survey is all that\u2019s necessary.<\/p>\n

A key premise behind factor analysis is the idea that many can be reduced to few.\u00a0 Imagine yourself in Munich for their annual Oktoberfest. You would undoubtedly see thousands people from all walks of life. Now, if I were to \u201cgroup\u201d these people based on some meaningful category \u2013 e.g., nationality, height, weight, or even the type of beer they are drinking \u2013 the resulting number of groups would be fewer than the thousands of individuals on which those groups are based. Factor analysis is very similar to this. Rather than people, however, we\u2019re now talking about survey questions.<\/p>\n

When you conduct a factor analysis on survey data collected from your employees, you\u2019re asking the program \u201cgroup\u201d the survey questions in some conceptually meaningful way.\u00a0 If you\u2019re thinking to yourself that survey questions are already organized into meaningful groups or categories \u2013 e.g., training, benefits, supervision, and so on \u2013 you\u2019re right.\u00a0 In fact, if the survey was designed properly and the factor analysis done correctly, you may find that factor analysis results show a perfect match between your survey items and your survey categories.\u00a0 Unfortunately, this will be rare. More often than not, you will find that a portion of the survey questions can be omitted, re-categorized or refined.<\/p>\n

Bottom line here is that when it comes to employee surveys, factor analysis is an important tool that can be used to help answer the question \u2013 Which questions should I keep or drop?\u00a0 It is an important step that will help to clarify the conclusions drawn from results of other advanced analyses typically conducted on survey data.<\/p>\n

Stephen B. Jeong, is currently the Managing Director of Waypoint People Solutions – www.waypointps.com<\/a>, a human capital consulting firm that focuses on high precision employee diagnostic surveys using cutting-edge measurement technology and methodologies. He holds Ph.D. in Industrial-Organizational psychology from the Ohio State University and has been advising private, public, and government organizations since 2000.\u00a0 He can be reached at stephen.jeong@waypointps.com.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Guest Author:\u00a0 Stephen B. Jeong, Ph.D. In Part 1 of Survey Design 101, we discussed two broad topics related to survey design \u2013 choosing the right topics and creating quality questions.\u00a0 Survey design (or questionnaire development), however, is not complete…<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[28,27,10],"tags":[58,73],"class_list":["post-1176","post","type-post","status-publish","format-standard","hentry","category-communications","category-data-metrics","category-engagement","tag-employee-engagement","tag-survey-design"],"_links":{"self":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1176","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1176"}],"version-history":[{"count":3,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1176\/revisions"}],"predecessor-version":[{"id":1337,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1176\/revisions\/1337"}],"wp:attachment":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1176"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1176"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1176"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}