It can be very exciting to receive those first few completed surveys back from respondents. Hopefully you\u2019ll even get more than a few back, and once you have a handful of completed questionnaires, your feelings may go from initial euphoria to dread. Data are fun and can also be overwhelming. The goal with data analysis is to be able to condense large amounts of information into usable and understandable chunks. Here we\u2019ll describe just how that process works for survey researchers.<\/p>\r\n
As mentioned, the hope is that you will receive a good portion of the questionnaires you distributed back in a completed and readable format. The number of completed questionnaires you receive divided by the number of questionnaires you distributed is your\u00a0response rate<\/em><\/strong>. Let\u2019s say your sample included 100 people and you sent questionnaires to each of those people. It would be wonderful if all 100 returned completed questionnaires, but the chances of that happening are about zero. If you\u2019re lucky, perhaps 75 or so will return completed questionnaires. In this case, your response rate would be 75% (75 divided by 100). That\u2019s pretty darn good.<\/p>\r\n Though response rates vary, and researchers don\u2019t always agree about what makes a good response rate, having three-quarters of your surveys returned would be considered good, even excellent, by most survey researchers. There has been lots of research done on how to improve a survey\u2019s response rate. Suggestions include personalizing questionnaires by, for example, addressing them to specific respondents rather than to some generic recipient such as \u201cmadam\u201d or \u201csir\u201d; enhancing the questionnaire\u2019s credibility by providing details about the study, contact information for the researcher, and perhaps partnering with agencies likely to be respected by respondents such as universities, hospitals, or other relevant organizations; sending out prequestionnaire notices and postquestionnaire reminders; and including some token of appreciation with mailed questionnaires even if small, such as a $1 coin.<\/p>\r\n The major concern with response rates is that a low rate of response may introduce\u00a0nonresponse bias\u00a0<\/em><\/strong>into a study\u2019s findings. What if only those who have strong opinions about your study topic return their questionnaires? If that is the case, we may well find that our findings don\u2019t at all represent how things really are or, at the very least, we are limited in the claims we can make about patterns found in our data. While high return rates are certainly ideal, a recent body of research shows that concern over response rates may be overblown (Langer, 2003). Several studies have shown that low response rates did not make much difference in findings or in sample representativeness (Curtin, Presser, & Singer, 2000; Keeter, Kennedy, Dimock, Best, & Craighill, 2006; Merkle & Edelman, 2002). For now, the jury may still be out on what makes an ideal response rate and on whether, or to what extent, researchers should be concerned about response rates. Nevertheless, certainly no harm can come from aiming for as high a response rate as possible.<\/p>","rendered":" It can be very exciting to receive those first few completed surveys back from respondents. Hopefully you\u2019ll even get more than a few back, and once you have a handful of completed questionnaires, your feelings may go from initial euphoria to dread. Data are fun and can also be overwhelming. The goal with data analysis is to be able to condense large amounts of information into usable and understandable chunks. Here we\u2019ll describe just how that process works for survey researchers.<\/p>\n As mentioned, the hope is that you will receive a good portion of the questionnaires you distributed back in a completed and readable format. The number of completed questionnaires you receive divided by the number of questionnaires you distributed is your\u00a0response rate<\/em><\/strong>. Let\u2019s say your sample included 100 people and you sent questionnaires to each of those people. It would be wonderful if all 100 returned completed questionnaires, but the chances of that happening are about zero. If you\u2019re lucky, perhaps 75 or so will return completed questionnaires. In this case, your response rate would be 75% (75 divided by 100). That\u2019s pretty darn good.<\/p>\n Though response rates vary, and researchers don\u2019t always agree about what makes a good response rate, having three-quarters of your surveys returned would be considered good, even excellent, by most survey researchers. There has been lots of research done on how to improve a survey\u2019s response rate. Suggestions include personalizing questionnaires by, for example, addressing them to specific respondents rather than to some generic recipient such as \u201cmadam\u201d or \u201csir\u201d; enhancing the questionnaire\u2019s credibility by providing details about the study, contact information for the researcher, and perhaps partnering with agencies likely to be respected by respondents such as universities, hospitals, or other relevant organizations; sending out prequestionnaire notices and postquestionnaire reminders; and including some token of appreciation with mailed questionnaires even if small, such as a $1 coin.<\/p>\n The major concern with response rates is that a low rate of response may introduce\u00a0nonresponse bias\u00a0<\/em><\/strong>into a study\u2019s findings. What if only those who have strong opinions about your study topic return their questionnaires? If that is the case, we may well find that our findings don\u2019t at all represent how things really are or, at the very least, we are limited in the claims we can make about patterns found in our data. While high return rates are certainly ideal, a recent body of research shows that concern over response rates may be overblown (Langer, 2003). Several studies have shown that low response rates did not make much difference in findings or in sample representativeness (Curtin, Presser, & Singer, 2000; Keeter, Kennedy, Dimock, Best, & Craighill, 2006; Merkle & Edelman, 2002). For now, the jury may still be out on what makes an ideal response rate and on whether, or to what extent, researchers should be concerned about response rates. Nevertheless, certainly no harm can come from aiming for as high a response rate as possible.<\/p>\n","protected":false},"author":84,"menu_order":7,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[48],"contributor":[],"license":[],"class_list":["post-219","chapter","type-chapter","status-publish","hentry","chapter-type-numberless"],"part":118,"_links":{"self":[{"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/pressbooks\/v2\/chapters\/219","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/wp\/v2\/users\/84"}],"version-history":[{"count":1,"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/pressbooks\/v2\/chapters\/219\/revisions"}],"predecessor-version":[{"id":220,"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/pressbooks\/v2\/chapters\/219\/revisions\/220"}],"part":[{"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/pressbooks\/v2\/parts\/118"}],"metadata":[{"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/pressbooks\/v2\/chapters\/219\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/wp\/v2\/media?parent=219"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/pressbooks\/v2\/chapter-type?post=219"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/wp\/v2\/contributor?post=219"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/opentextbooks.concordia.ca\/quantitativeresearch\/wp-json\/wp\/v2\/license?post=219"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}