Enhancing Surveys of Health Care Professionals

Abstract

Surveys involving health care providers are characterized by low and declining response rates (RRs), and researchers have utilized various strategies to increase survey RRs among health professionals. Based on 48 studies with 156 subgroups of within-study conditions, a multilevel meta-regression analysis was conducted to summarize the effects of different strategies employed in surveys of health professionals. An estimated overall survey RR among health professionals was 0.53 with a significant downward trend during the last half century. Of the variables that were examined, mode of data collection, incentives, and number of follow-up attempts were all found to be significantly related to RR. The mail survey mode was more effective in improving RR, compared to the online or web survey mode. Relative to the non-incentive subgroups, subgroups receiving monetary incentives were more likely to respond, while nonmonetary incentive groups were not significantly different from non-incentive groups. When number of follow-ups was considered, the one or two attempts of follow-up were found to be effective in increasing survey RR among health professionals. Having noted challenges associated with surveying health professionals, researchers must make every effort to improve access to their target population by implementing appropriate incentive- and design-based strategies demonstrated to improve participation rates.

Keywords

health professionals survey response rates research methods meta-analysis

Introduction

Surveys of physicians, nurses, and allied health professionals are widely used as means to inform health policy, public health, and health services delivery across an array of issues. Despite their importance, however, surveys of health professionals can be challenging. Studies involving health care providers generally—and physicians specifically—are characterized by low or declining response rates (RRs; Cook, Dickinson, & Eccles, 2009; Cull, O’Connor, Sharp, & Tang, 2005; Hill, Fahrney, Wheeless, & Carson, 2006; McLeod, Klabunde, Willis, & Stark, 2013; Ulrich & Grady, 2004). While insufficient as a single indicator of survey quality, poor RRs still pose problems, as they have significant implications for sample size, cost, and potential nonresponse bias (i.e., the likelihood of systematic differences between those who returned a survey and those who did not). Nonresponse bias, in particular, can impact the generalizability or applicability of study results (Asch, Jedrziewski, & Christakis, 1997; Barriball & While, 1999; Cull et al., 2005; Cummings, Savitz, & Konrad, 2001; Curtis & Redmond, 2009; Templeton, Deehan, Taylor, Drummond, & Strang, 1997), as well as have probable consequences for measurement quality (Olson, 2006). Despite these challenges, surveys are still preferred as a cost-effective means of gathering information on care delivery. In response, researchers have utilized a number of techniques designed to stimulate survey RRs among health professionals, as well as to improve the overall quality of surveys in these populations (Klabunde et al., 2012).

Strategies employed to improve the quality of surveys of health professionals generally fall into either design- or incentive-based interventions and include monetary and nonmonetary response incentives, personalized mailings, prompts and follow-up contacts, postage, and sponsorship. Many of these interventions are derived from the tailored design method, which provides guidelines for instrument development (i.e., surveys) and also specifies the type and timing for initial contact, follow-up mailings, telephone follow-up, and incentives in order to stimulate response (Dillman, Smyth, & Christian, 2009). Although limited to physicians and nurses, systematic reviews have found a number of these practices to be successful in inducing response (Field et al., 2002; Kellerman & Herold, 2001; Klabunde et al., 2012; Martins et al., 2012; VanGeest & Johnson, 2011; VanGeest, Johnson, & Welch, 2007). Monetary incentives, in particular, have been shown to be effective in increasing clinician response. Moreover, even modest US$1 incentives are effective, with diminishing returns to serial increments above that amount (VanGeest et al., 2007). Token nonmonetary incentives (e.g., continuing medical education credits, pens, and candy), on the other hand, appear to have little or no impact unless sufficiently valued. A number of design-based interventions are also identified as effective in improving health professional response, including use of mixed-mode formats, brevity in questionnaire design, personalization, adequate follow-up contacts, and use of certified mail and/or courier companies such as FedEx. Consensus on applicability of best practices for clinician surveys, however, remains elusive, with more needed to be known on issues related to incentivizing providers, as well as key design aspects to improve the quality of clinician surveys (Klabunde et al., 2012).

Although helpful, systematic reviews are limited in that they typically fail to take into account explicit differences in studies due to sample size, composition, or study design. A meta-analysis offers distinct advantages in enabling us to address these limitations. In a meta-analysis, the findings of each study are treated as an independent observation, which may be combined to allow examination of overall or average effect. Additionally, by controlling for the unique properties of studies, the meta-analytic technique allows for the examination of potential sources of between study heterogeneity. In this article, we report a meta-analysis conducted to determine which incentive- and/or design-based strategies are most effective in improving clinician participation in survey research.

Method

Search Strategy and Study Selection

Experimental studies examining methods to improve clinician response to surveys were identified through key word searches of the MEDLINE, Scopus, Sociological Abstracts, and PsychINFO databases from 1958 to 2013. Searches by author using the same databases were also conducted for investigators with identified relevant articles. Finally, several seed sources (e.g., Evaluation and the Health Professions, BMC Medical Research Methodology, Public Opinion Quarterly, Medical Care, Health Services Research, Nursing Research, and Applied Nursing Research) were also referenced manually in an effort to establish a comprehensive set of studies to be included in the analyses. Further relevant articles and books were selected from the reference listings of the primary journal articles. Additional reports, conference papers, and other related resources were identified through online searches on Google, Google Scholar, and other Internet searches of specific organizations known to support survey research (e.g., American Association for Public Opinion Research, American Statistical Association, etc.).

All authors reviewed unique entries from the database and literature searches based on the following eligibility criteria for population (physicians, medical school faculty, medical students, nurses, nursing school faculty, nursing students, pharmacists, and other allied health professionals), studies that report design- or incentive-based strategies to improve survey response, outcome (RR), and study design (randomized controlled trial [RCT], case-control study, case studies, etc., examining the effectiveness of strategies of interest to improve clinician survey participation). Descriptive observational studies were excluded. Studies that did not report subgroup-specific sample sizes, without which we cannot estimate sample variance, were also not included. We identified a total of 48 studies with 156 subgroups within studies (see Appendix).

Data Extraction

The primary author (Y.C.) independently conducted the abstraction of data. Where necessary, questions were resolved via discussion and/or consultation with the other coauthors (T.J. and J.V.G.). Data regarding characteristics of the sample, intervention, and outcomes of interest were extracted. Specific variables coded for the meta-analysis included year of publication, location where survey was conducted (United States or elsewhere), type of respondents (e.g., physicians, nurses, and allied health professionals), mode of data collection, incentives (no incentives, monetary, or nonmonetary compensation), follow-up contact attempts, and subgroup-specific sample size and RRs. With regard to the primary outcome of interest, the RR was calculated as a proportion of all eligible respondents who completed the survey in order to have a consistent and comparable measure between studies.

Statistical Analysis

We conducted multilevel meta-analysis with variance known models (Raudenbush & Bryk, 2002), using hierarchical linear and nonlinear modeling 7 (Raudenbush et al., 2011). With these hierarchical models, we can estimate an average survey RR after accounting for random variations between studies and between subgroups within each study, and simultaneously controlling for the effects of subgroup- and study-level attributes of interest. The models are specified as following:

Level-1 (Within-Subgroup) Model

L n {(responserate)}_{i j k} = π_{0 j k} + e_{i j k},

for subgroups $j = 1, \dots, J$ , where e_ijk is the sampling error associated with the RR as an estimate of population parameter, π_0jk, for which the normality assumption is applied with known variance, that is, $e_{i j k} \sim N (0, V_{j})$ . Note that the RR is treated as proportion and logit transformed, and V_j is the sampling variance of the RR.

Level-2 (Between-Subgroups) Model

\begin{aligned} π_{0 j k} = β_{00 k} + β_{01 k} ({m o d e : o n l i n e}_{j k}) + β_{02 k} ({m o d e : m i x e d}_{j k}) \\ + β_{03 k} (m o n e t a r y {i n c e n t i v e}_{j k}) + β_{04 k} (n o n m o n e t a r y {i n c e n t i v e s}_{j k}) \\ + β_{05 k} ({n u r s e s}_{j k}) + β_{06 k} (o t h e r h e a l t h {p r o f e s s i o n a l s}_{j k}) \\ + β_{07 k} (f o l l o w - u p 1_{j k}) + β_{08 k} (f o l l o w - u p 2_{j k}) \\ + β_{09 k} (f o l l o w - u p {3 p l s}_{j k}) + r_{0 j k}, \end{aligned}

where the true RR of the population, π_0jk, depends on subgroup-level attributes (i.e., data collection mode, monetary or nonmonetary incentives, type of respondents, and number of follow-ups) and a Level-2 random error, r _0jk for which we assume $r_{0 j k} \sim N (0, τ_{π})$ .

Level-3 (Between-Studies) Model

\begin{aligned} β_{00 k} {= γ}_{000} + γ_{001} ({p u b l i c a t i o n y e a r}_{k}) + γ_{002} ({r a n d o m i z e d c o n t r o l l e d t r i a l}_{k}) \\ + γ_{003} ({U S A}_{k}) + u_{00 k}, \end{aligned}

where γ₀₀₁, γ₀₀₂, and γ₀₀₃ are regression coefficients of study characteristics, publication year, RCT versus non-RCT study, and the place of study conducted, respectively. u _00k is a Level-3 random effect with the normality assumption, $u_{00 k} \sim N (0, τ_{β})$ .

Results

Descriptive statistics for the variables included in the model are presented in Table 1. An overall average RR among 154 subgroups of studies was 0.50 (SD = 0.22). Mail surveys were used for 111 subgroups (72%). Mixed modes of data collection were used for 23 subgroups (15%). Surveys for the remaining 20 subgroups were conducted via online or web. In the multilevel model, two nominal variables were included representing (1) online or web subgroups and (2) mixed-mode subgroups, with an omitted category of mail subgroups as the reference category. Forty-seven percent of the subgroups received monetary incentives and 9% received nonmonetary compensation. In terms of respondent type, there were 104 subgroups (67%) of medical doctors, faculty, or medical students, 34 subgroups (21%) of nurses, nurse practitioners, nursing students, or faculty, and 18 subgroups consisting of other health professionals, such as dentists, pharmacists, and so on (11.7%). Of the 154 subgroups, 46 (30%) were not followed up after the initial survey request. Twenty-six (17%) and 41 subgroups (27%) were categorized as being followed-up once and twice, respectively. The remaining 41 subgroups were followed-up 3 or more times. At the study level, we found that a majority were published after the year 2002. Twenty-eight studies were conducted with RCT design (66.7%) and the remaining third employed non-RCT designs. Most studies (72.4%) were conducted with U.S. samples.

Table 1.

Descriptive Statistics.

Variables	N	Mean or percentage
Response rate (proportion; range: 0.03–0.97)	154	0.50 (SD = 0.22)
Study subgroup level	154
Data collection mode
Mail	111	72.1
Online	20	13.0
Mixed mode	23	14.9
Incentives
No incentives	69	44.8
Monetary	73	47.4
Nonmonetary	14	9.1
Type of respondents
Physicians	104	67.5
Nurses	34	20.8
Other health professionals	18	11.7
Follow-up
No follow-up	46	29.9
One follow-up	26	16.9
Two follow-ups	41	26.6
Three or more follow-ups	41	26.6
Study level	48
Year published (range: 1958–2012)		2001 (Mdn = 2002)
Study design
Randomized controlled trial (RCT)	28	58.3
Non-RCT	20	41.7
Location of survey
United States	33	68.8
Other countries	15	31.3

As presented in Table 2, multilevel meta-analysis estimated that an overall survey RR among health professionals was 53% (b = 0.127, standard error [SE] = 0.118) after controlling for study subgroup and study characteristics and adjusting for random variance between subgroups and studies. The random variances at both levels were found to be highly significant as indicated at the bottom of Table 2. The random variances at the study subgroup level and study level without controlling for any attributes were 0.336 (χ² = 3502.6; p < .001) and 0.653 (χ² = 276.6; p < .001), respectively.

Table 2.

Multilevel Meta-Regression Coefficients for Survey Response Rates Among Health Professionals.

Predictors	Coefficient (b)	Standard error (SE)	t Ratio	p Value
Intercept	0.127	0.118	1.079	.286
Study subgroup level
Data collection mode (ref = mail)
Online	−0.746	0.315	−2.369	.020
Mixed mode	−0.304	0.379	−0.801	.425
Incentives (ref = no incentives)
Monetary	0.474	0.141	3.363	.001
Nonmonetary	−0.058	0.199	−0.293	.770
Type of respondents (ref = physicians)
Nurses	−0.160	0.232	−0.688	.493
Other health professionals	−0.353	0.209	−1.689	.094
Follow-up (ref = no follow-up)
1 time	0.557	0.156	3.563	<.001
2 times	0.923	0.232	3.971	<.001
3 or more times	0.223	0.388	0.576	.566
Study level
Year published	−0.032	0.010	−3.054	.004
Study design: RCT (vs. non-RCT)	−0.297	0.237	−1.254	.216
Location of survey: United States (vs. other)	−0.564	0.271	−2.084	.043
Random effects	Standard deviation	Variance component	χ²	p Value
Study group level	0.458	0.209	2,399.84	<.001
Study level	0.707	0.499	296.26	<.001

Note. RCT = randomized controlled trial.

Of the variables that were examined at the study subgroup level, mode of data collection, incentives, and number of follow-ups were found to be significantly related to RR. The estimated RR for the mail survey mode was 57% (see Figure 1A), which was significantly higher compared to the online or web survey mode (RR = 38%). The difference between mail and mixed survey modes was not statistically significant. Compared to the non-incentive subgroup, those receiving monetary incentives had significantly higher RRs (b = 0.474, SE = 0.141). The estimated RRs for non-incentivized versus monetarily incentivized groups were 48% and 60%, respectively (see Figure 1B). The subgroup with nonmonetary incentives did not differ from the non-incentive subgroup. Relative to the medical doctors group (RR = 55%), RRs among nurses (RR = 51%) were not significantly different, and other allied health professionals (RR = 46%) had slightly lower RRs compared to medical doctors, a difference that was marginally significant (b = −0.353; SE = 0.209; see Figure 1C). When four levels of follow-up were considered, the one (RR = 57%) and two attempt (RR = 66%) follow-up subgroups showed significantly higher RRs compared to the no-follow-up subgroup (RR = 43%). Those with three or more follow-ups (RR = 49%) were not significantly different from the no-follow-up subgroup (RR = 43%; see Figure 1D).

Figure 1.

Estimated response rates by study subgroup-level characteristics.

Generally, there was a downward trend in terms of survey RR across years, indicating that there was a significant negative association between recency of the study and RR (Figure 2A). The study design (i.e., RCT vs. non-RCT) was not found to be associated with the RR. RRs of surveys conducted in the United States were slightly lower than those conducted elsewhere (b = −0.474; SE = 0.268, p = .084).

Figure 2.

Estimated response rates by study-level characteristics.

Discussion

Our analysis of strategies for improving clinician survey response presents some interesting, although not unexpected, results. What is unique is the use of meta-analysis, a replicable method of synthesizing and analyzing findings from across multiple studies to further identify evidence-based strategies most likely to improve clinician survey participation. Of the various techniques analyzed, use of financial incentives clearly improves survey participation among health care professionals. This is consistent with recent systematic reviews examining monetary incentives in surveys of clinicians and medical groups (Burns et al., 2008; Klabunde et al., 2012; McLeod et al., 2013; VanGeest et al., 2007; VanGeest & Johnson, 2011). While the optimal incentive amount remains elusive, incentives as small as US$1 may be sufficient to improve participation, with previously noted diminishing returns above that amount (Halpern, Ubel, Berlin, & Asch, 2002; VanGeest et al., 2007). McLeod, Klabunde, Willis, and Stark (2013), in their review of large health care provider surveys, did find that incentives over US$30 were more likely to achieve a target 60% RR compared to those offering a lower or no incentives. This same review also supported the use of prepaid versus contingent incentives in improving clinician participation. Additionally, with regard to our findings for subgroups receiving nonmonetary incentives, this too is consistent with previous evidence suggesting that these inducements work only if physicians find them of sufficient value (VanGeest et al., 2007).

While effective, incentives can add a sizable component to the overall costs of administering a survey. Their advantage lies in potential cost savings associated with reduced follow-up. In addition, there are ethical issues associated with employing incentives. The National Cancer Institute’s (NCI) workshop on surveys of physicians and medical groups expressed concern about escalating incentive amounts and the potential for incentives to influence item response; ultimately concluding that incentives remain an important—and cost effective—means of communicating the importance of survey participation (Klabunde et al., 2012). Ethical analyses further suggest that the key factor is whether or not incentives can be considered coercive; a judgment that entails balancing potential risks and harms of a study and informed consent with the incentive amount employed (Singer & Couper, 2008).

The two principal design-based interventions shown to be effective in improving participation—survey mode and follow-up—are also consistent with previous research. Evidence supporting mode effects in clinician survey participation is substantial (Klabunde et al., 2012; McLeod et al., 2013; VanGeest et al., 2007; VanGeest & Johnson, 2011). Despite the dramatic increase in use of information technologies generally in our society, postal surveys still typically result in higher average return rates among health professionals. Existing evidence also supports the use of mixed-mode or sequential mixed-mode formats that include fax and possibly e-mail options, as these give health professionals more alternatives by which to respond; allowing them greater flexibility to fit the survey within their busy schedules. Caution may still be necessary when considering web or electronic survey modes, as researchers need to be cognizant of the capabilities and/or habits of the target populations, as well as the availability of valid e-mail addresses in sample frames. The potential for item nonresponse may also higher in online surveys, particularly if it involves sending confidential information; due to familiarity with and trust in the Internet (Scott et al., 2011). The NCI workshop, noting poor availability of valid e-mail addresses overall as well as barriers (both institutional and individual) to electronic or web-based administration of surveys, recommended focusing on the practice environment specifically, particularly gatekeepers. Additionally, they point to the need for more information on why clinicians react to particular modes and to inform when it is appropriate to utilize electronic administration options (Klabunde et al., 2012).

Regarding the need for follow-up, there is consensus in the literature that multiple contacts may be necessary in maximizing clinician survey participation (Klabunde et al., 2012). For example, in one study, over 30% of the completed surveys were obtained after one contact, but another 20% required 11 or more contact attempts (Parsons, Warnecke, Czaja, Barnsley, & Kaluzny, 1994). This same study highlighted differences in number of contact attempts by mode of data collection, with only 6% of telephone interviews completed after one contact attempt compared with 60% finalized mail surveys in one mailing of the questionnaire. Similarly, the NCI workshop participants estimated that one third or more of final data will likely be obtained through follow-up (Klabunde et al., 2012). Tailoring or personalizing this follow-up may further improve response, but this may entail cost/quality trade-offs as some options are expensive. Follow-up does have its limits, with some diminishing returns associated with efforts to convert particularly difficult cases (Berk, 1985; Cull et al., 2005).

Finally, of note is the demonstrated decline in RRs between 1958 and 2012. Clinicians are busy, with surveys competing against other—arguably more important—priorities. Lack of time, is compounded by the increasing volume of surveys clinicians are asked to respond to, as well as practice environments (with various gatekeepers) serving as additional barriers to survey participation. Regardless, the trend noted here is a stark reminder of the challenges associated with surveying health professionals. As a result, researchers must make every effort to improve access to their target population by implementing appropriate incentive- and design-based strategies demonstrated to improve participation rates.

Limitations

Other reviews and studies have explored other options, particularly design-based strategies that may also contribute to improved clinician survey response, including questionnaire length and type of mailing. Limitations related to method and study selection criteria in the present study constrain the full range of interventions considered. Klabunde et al. (2012) present a wider research agenda to further our understanding of which interventions (or combination of interventions) will contribute to higher RRs. Examples extend beyond different design-based interventions to include our inability to differentiate between incentive amounts. This is critical given the obvious cost implications of incentives in planning studies of health care professionals. While incentives work, research is still needed to determine to most effective amount relative to the populations of interest (Klabunde et al., 2012). Additionally, there is a clear lack of diversity in our “health care provider” category. The overwhelming proportion of available studies involves physician samples, severely limiting our ability to test between-group differences in provider response to various incentive- or design-based interventions.

Ultimately, decisions regarding what methodologies to employ are embedded within cost-quality trade-offs. The good news is that previous reviews identified smaller than anticipated differences between clinician respondents and nonrespondents and between early and late responders (Field et al., 2002; Kellerman & Herold, 2001; McFarlane, Olmsted, Murphy, & Hill, 2006), suggesting low rates of nonresponse bias due to the within-group homogeneity of clinicians—nurses and physicians specifically—with regard to their respective knowledge, training, attitudes, and behavior (Barriball & While, 1999; Field et al., 2002; Ford & Bammer, 2009; Kellerman & Herold, 2001). We also wish to acknowledge that RRs are no longer understood to be the optimal indicator of survey quality. Rather, nonresponse bias, which is not necessarily correlated with RR, is of greater importance for evaluating the validity of survey findings (Johnson & Wislar, 2012). Future research should focus on the methodological correlates of nonresponse bias in surveys of health care professionals.

Footnotes

Appendix

Summary of Studies.

Study	Lead author	Year published	Place of survey	Design	Subgroup	N eligible	N completed	Response rate^a	Sample variance	Mode	Incentive	Follow-ups	Respondent type
1	Akl	2005	United States	Non-RCT	1	60	38	0.633	0.113	Online	No incentives	2	Resident
					2	59	47	0.797	0.131	Mail	No incentives	2	Resident
					3	41	35	0.854	0.228	Online	No incentives	2	Faculty
					4	42	34	0.810	0.190	Mail	No incentives	2	Faculty
2	Asch	1994	United States	RCT	1	452	154	0.341	0.028	Mail	No incentives	0	Physician
2	Asch	1994	United States	RCT	2	449	184	0.410	0.022	Mail	No incentives	0	Physician
3	Asch	1996	United States	RCT	1	830	635	0.765	0.008	Mail	Monetary	2	Nurse
3	Asch	1996	United States	RCT	2	730	504	0.690	0.009	Mail	Monetary	2	Nurse
4	Asch	1998	United States	RCT	1	482	221	0.459	0.018	Mail	Monetary	3+	Physician
4	Asch	1998	United States	RCT	2	484	296	0.612	0.014	Mail	Monetary	3+	Physician
5	Becker	2000	United States	Non-RCT	1	833	286	0.343	0.015	Mail	No incentives	0	Nurse
					2	596	217	0.364	0.019	Mail	No incentives	0	Nurse
					3	833	322	0.387	0.013	Mail	No incentives	1	Nurse
					4	596	232	0.389	0.018	Mail	No incentives	1	Nurse
					5	833	356	0.427	0.011	Mail	No incentives	2	Nurse
					6	596	273	0.458	0.014	Mail	No incentives	2	Nurse
6	Beebe	2007	United States	RCT	1	245	154	0.629	0.027	Mixed	No incentives	2	Physician
6	Beebe	2007	United States	RCT	2	244	172	0.705	0.028	Mixed	No incentives	2	Physician
7	Berk	1985	United States	Non-RCT	1	6,685	4,947	0.740	0.001	Mail	No incentives	2	Physician
8	Berry	1987	United States	RCT	1	1,011	783	0.774	0.007	Mail	Monetary	3+	Physician
8	Berry	1987	United States	RCT	2	1,017	670	0.659	0.006	Mail	Monetary	3+	Physician
9	Bhandari	2003	Canada	RCT	1	196	117	0.597	0.035	Mail	No incentives	0	Surgeon
9	Bhandari	2003	Canada	RCT	2	199	93	0.467	0.043	Mail	No incentives	0	Surgeon
10	Bonevski	2011	Australia	RCT	1	1,617	451	0.279	0.011	Mixed	No incentives	1	Physician
					2	587	27	0.046	0.844	Mixed	Monetary	2	Physician
					3	576	22	0.038	1.237	Mixed	Monetary	1	Physician
11	Bostick	1992	Australia	Non-RCT	1	262	172	0.656	0.025	Online	No incentives	0	Physician
11	Bostick	1992	Australia	Non-RCT	2	262	241	0.920	0.056	Online	No incentives	1	Physician
12	Camuňas	1990	Canada	RCT	1	100	16	0.160	0.465	Mail	No incentives	0	Nurse
					2	100	11	0.110	0.928	Mail	No incentives	0	Nurse
					3	100	21	0.210	0.287	Mail	No incentives	0	Nurse
					4	200	90	0.450	0.044	Mail	Monetary	0	Nurse
					5	200	42	0.210	0.143	Mail	No incentives	0	Nurse
13	Cartwright	1968	England	RCT	1	57	55	0.965	0.537	Mail	No incentives	3+	Physician
					2	59	46	0.780	0.126	Mail	No incentives	3+	Physician
					3	58	48	0.828	0.146	Mail	No incentives	3+	Physician
					4	58	39	0.672	0.116	Mail	No incentives	3+	Physician
14	del Valle	1997	United States	Non-RCT	1	1,750	1,336	0.763	0.004	Mail	No incentives	2	Physician
					2	143	59	0.413	0.069	Mail	No incentives	3+	Physician
					3	266	66	0.248	0.081	Mail	No incentives	3+	Physician
15	Donaldson	1999	United States	RCT	1	100	48	0.480	0.083	Mail	No incentives	0	Physician
					2	100	55	0.550	0.073	Mail	Monetary	0	Physician
					3	100	43	0.430	0.094	Mixed	No incentives	1	Physician
					4	100	60	0.600	0.069	Mixed	Monetary	1	Physician
16	Dykema	2011	United States	RCT	1	500	15	0.030	2.291	Mail	No incentives	0	Physician
					2	500	31	0.062	0.554	Mail	No incentives	0	Physician
					3	500	31	0.062	0.554	Mail	Monetary	0	Physician
					4	500	33	0.066	0.491	Mail	Monetary	0	Physician
					5	500	43	0.086	0.295	Mail	Monetary	0	Physician
					6	350	47	0.134	0.183	Mail	Monetary	0	Physician
					7	350	54	0.154	0.141	Mail	Monetary	0	Physician
					8	350	89	0.254	0.059	Mail	Monetary	0	Physician
17	Everett	1997	United States	RCT	1	261	164	0.628	0.026	Mail	Monetary	0	Physician
17	Everett	1997	United States	RCT	2	260	118	0.454	0.034	Mail	No incentives	0	Physician
18	Gattellari	2001	Australia	RCT	1	108	91	0.843	0.082	Mail	Monetary	0	Surgeon
18	Gattellari	2001	Australia	RCT	2	111	104	0.937	0.162	Mail	No incentives	0	Surgeon
19	Gore-Felton	2002	United States	Non-RCT	1	987	671	0.680	0.006	Mail	No incentives	2	Psychologist
20	Grava-Gubins	2008	Canada	Non-RCT	1	60,811	19,239	0.316	0.000	Mixed	Monetary	3+	Physician
					2	9,162	2,822	0.308	0.001	Online	Monetary	3+	Medical Student
					3	2,627	733	0.279	0.006	Online	Monetary	3+	Resident
21	Guise	2010	England	Non-RCT	1	148	48	0.324	0.095	Online	No incentives	3+	Nurse
21	Guise	2010	England	Non-RCT	2	148	81	0.547	0.049	Mixed	No incentives	3+	Nurse
22	Halpern	2011	United States	RCT	1	1,382	422	0.305	0.011	Online	Monetary	2	Physician
					2	414	129	0.312	0.036	Online	Monetary	2	Physician
					3	410	133	0.324	0.034	Online	No incentives	2	Physician
					4	738	291	0.394	0.014	Mail	Monetary	1	Nurse
					5	250	147	0.588	0.028	Mail	Monetary	1	Nurse
					6	385	183	0.475	0.021	Online	Monetary	2	Physician
					7	400	223	0.558	0.018	Online	Monetary	2	Physician
23	Hart	2009	United States	Non-RCT	1	236	116	0.492	0.034	Online	No incentives	1	Nurse program director
23	Hart	2009	United States	Non-RCT	2	76	34	0.447	0.119	Online	No incentives	1	Nurse program director
24	Hawley	2009	United States	Non-RCT	1	98	39	0.398	0.107	Mail	No incentives	2	Mental health provider
					2	98	41	0.418	0.100	Mail	Nonmonetary	2	Mental health provider
					3	100	51	0.510	0.078	Mail	Monetary	2	Mental health provider
					4	99	60	0.606	0.069	Mail	Monetary	2	Mental health provider
					5	99	64	0.646	0.068	Mail	Monetary	2	Mental health provider
25	Heywood	1995	Canada	Non-RCT	1	16	7	0.438	0.580	Mail	No incentives	2	Physician
					2	172	115	0.669	0.039	Mail	No incentives	3+	Physician
					3	69	56	0.812	0.116	Mail	No incentives	2	Physician
26	James	2011	United States	Non-RCT	1	263	90	0.342	0.049	Mail	Monetary	2	Physician
					2	255	50	0.196	0.126	Mail	Monetary	2	Physician
					3	265	26	0.098	0.434	Mail	Monetary	2	Physician
					4	266	20	0.075	0.719	Mail	Monetary	2	Physician
27	Kanaan	2010	England	RCT	1	214	151	0.706	0.031	Mail	Monetary	1	Physician
					2	147	79	0.537	0.050	Mail	Monetary	1	Physician
					3	230	121	0.526	0.033	Mail	No incentives	1	Physician
28	Keating	2008	United States	RCT	1	549	286	0.521	0.014	Mixed	Monetary	3+	Physician
28	Keating	2008	United States	RCT	2	431	292	0.677	0.015	Mixed	Monetary	3+	Physician
29	Kephart	1958	United States	RCT	1	100	52	0.520	0.077	Mail	No incentives	0	Nurse
					2	100	53	0.530	0.075	Mail	No incentives	0	Nurse
					3	100	68	0.680	0.067	Mail	No incentives	1	Nurse
					4	100	67	0.670	0.067	Mail	No incentives	1	Nurse
					5	100	60	0.600	0.069	Mail	No incentives	0	Nurse
					6	100	66	0.660	0.067	Mail	No incentives	0	Nurse
					7	100	55	0.550	0.073	Mail	Monetary	0	Nurse
					8	100	54	0.540	0.074	Mail	Monetary	0	Nurse
					9	100	57	0.570	0.071	Mail	Monetary	0	Nurse
					10	100	70	0.700	0.068	Mail	Monetary	0	Nurse
30	Lusk	2007	United States	RCT	1	4,887	3,201	0.655	0.001	Mail	Monetary	3+	Health professionals
30	Lusk	2007	United States	RCT	2	3,527	328	0.093	0.036	Online	Monetary	3+	Health professionals
31	Malin	2000	United States	Non-RCT	1	169	28	0.166	0.258	Mail	No incentives	0	Medical director
					2	169	46	0.272	0.109	Mixed	No incentives	1	Medical director
					3	165	124	0.752	0.043	Mixed	Monetary	2	Medical director
32	Martins	2012	United States	Non-RCT	1	190	84	0.442	0.048	Mixed	Monetary	1	Physician
32	Martins	2012	United States	Non-RCT	2	190	131	0.689	0.035	Mixed	Monetary	2	Physician
33	Matteson	2011	United States	Non-RCT	1	802	262	0.327	0.017	Online	No incentives	3+	Physician
33	Matteson	2011	United States	Non-RCT	2	802	417	0.520	0.009	Mixed	No incentives	3+	Physician
34	McLaren	2000	Australia	RCT	1	305	188	0.616	0.022	Mail	Nonmonetary	2	Physician
					2	316	194	0.614	0.021	Mail	Nonmonetary	2	Physician
					3	311	197	0.633	0.021	Mail	Nonmonetary	2	Physician
					4	310	185	0.597	0.022	Mail	Nonmonetary	2	Physician
35	Myerson	1993	England	Non-RCT	1	85	50	0.588	0.082	Mail	No incentives	0	Physician
					2	85	63	0.741	0.082	Mail	No incentives	1	Physician
					3	85	72	0.847	0.107	Mail	No incentives	2	Physician
36	Nicholls	2011	United States	Non-RCT	1	2,378	609	0.256	0.008	Mixed	Monetary	3+	Physician
37	Olson	1993	United States	RCT	1	5,500	1,293	0.235	0.004	Mixed	Monetary	3+	Physician
					2	5,500	1,122	0.204	0.005	Mixed	No incentives	3+	Physician
					3	611	426	0.697	0.011	Mail	Monetary	0	Physician
					4	624	405	0.649	0.010	Mail	Monetary	0	Physician
					5	629	438	0.696	0.010	Mail	Monetary	0	Physician
38	Paul	2005	Australia	RCT	1	334	220	0.659	0.020	Mail	Monetary	2	Pharmacist
38	Paul	2005	Australia	RCT	2	331	177	0.535	0.022	Mail	No incentives	2	Pharmacist
39	Puleo	2002	United States	Non-RCT	1	761	286	0.376	0.014	Mail	Monetary	0	Physician
					2	761	460	0.604	0.009	Mail	Monetary	1	Physician
					3	761	509	0.669	0.008	Mail	Monetary	2	Physician
					4	761	621	0.816	0.010	Mixed	Monetary	3+	Physician
40	Recklitis	2009	United States	RCT	1	136	111	0.816	0.060	Mixed	Monetary	1	Physician
					2	135	85	0.630	0.050	Mixed	Nonmonetary	1	Physician
					3	135	103	0.763	0.053	Mixed	Monetary	1	Physician
41	Temple-Smith	1998	Australia	Non-RCT	1	520	294	0.565	0.013	Mail	Nonmonetary	0	Physician
					2	520	397	0.763	0.013	Mail	Nonmonetary	1	Physician
					3	520	444	0.854	0.018	Mail	Nonmonetary	2	Physician
42	Thorpe	2008	United States	Non-RCT	1	98	72	0.735	0.071	Mail	Monetary	3+	Physician
					2	239	182	0.762	0.030	Mail	Monetary	3+	Physician
					3	320	154	0.481	0.026	Mail	No incentives	3+	Physician
					4	2,240	1,647	0.735	0.003	Mail	Monetary	3+	Physician
43	Ulrich	2005	United States	RCT	1	470	226	0.481	0.017	Mail	No incentives	3+	Nurse
					2	491	178	0.363	0.024	Mail	No incentives	3+	Physician aid
					3	474	231	0.487	0.017	Mail	Monetary	3+	Nurse
					4	508	207	0.407	0.020	Mail	Monetary	3+	Physician aid
					5	496	345	0.696	0.013	Mail	Monetary	3+	Nurse
					6	523	307	0.587	0.013	Mail	Monetary	3+	Physician aid
44	VanGeest	2001	United States	RCT	1	292	176	0.603	0.023	Mail	Monetary	3+	Physician
					2	291	198	0.680	0.023	Mail	Monetary	3+	Physician
					3	290	189	0.652	0.023	Mail	Monetary	3+	Physician
45	Viera	2012	United States	RCT	1	4,245	509	0.120	0.018	Online	Monetary	0	Physician
45	Viera	2012	United States	RCT	2	4,232	703	0.166	0.010	Online	Monetary	0	Physician
46	VonRiesen	1979	United States	RCT	1	396	166	0.419	0.024	Mail	No incentives	1	Veterinarian
					2	390	195	0.500	0.020	Mail	No incentives	1	Veterinarian
					3	392	155	0.395	0.027	Mail	No incentives	0	Veterinarian
47	Wilson	2010	Canada	RCT	1	244	122	0.500	0.032	Online	Nonmonetary	3+	Researcher
47	Wilson	2010	Canada	RCT	2	241	110	0.456	0.036	Online	Nonmonetary	3+	Researcher
48	Ziegenfuss	2012	United States	RCT	1	620	88	0.142	0.093	Mail	Monetary	0	Physician
48	Ziegenfuss	2012	United States	RCT	2	630	100	0.159	0.074	Mail	Monetary	0	Physician

Note. ^aProportion.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Akl

E. A.

Maroun

Klocke

R. A.

Montori

Schunemann

H. J.

(2005). Electronic mail was not better than postal mail for surveying residents and faculty. Journal of Clinical Epidemiology, 58, 425–429.

Asch

D. A.

(1996). Use of a coded postcard to maintain anonymity in a highly sensitive mail survey: Cost, response rates, and bias. Epidemiology, 7, 550–551.

Asch

D. A.

Christakis

N. A.

(1994). Different response rates in a trial of two envelop styles in mail survey research. Epidemiology, 5, 364–365.

Asch

D. A.

Christakis

N. A.

Ubel

P. A.

(1998). Conducting physician mail surveys on a limited budget: A randomized trial comparing $2 bill versus $5 bill incentives. Medical Care, 36, 95–99.

Asch

D. A.

Jedrziewski

M. K.

Christakis

N. A.

(1997). Response rates to mail surveys published in medical journals. Journal of Clinical Epidemiology, 50, 1129–1136.

Barriball

K. L.

While

A. E.

(1999). Non-response in survey research: A methodological discussion and development of an explanatory model. Journal of Advanced Nursing, 30, 677–686.

Becker

Cookston

Kulberg

(2000). Mailed survey follow-ups—Are postcard reminders more cost-effective than second questionnaires? Western Journal of Nursing Research, 22, 642–647.

Beebe

T. J.

Locke

III Barnes

S. A.

Davern

M. E.

Anderson

K. J.

(2007). Mixing web and mail methods in a survey of physicians. Health Services Research, 42, 1219–1234.

Berk

M. L.

(1985). Interviewing physicians: The effect of improved response rate. American Journal of Public Health, 75, 1338–1340.

10.

Berry

S. H.

Kanouse

D. E.

(1987). Physician response to a mailed survey: An experiment in timing of payment. Public Opinion Quarterly, 51, 102–114.

11.

Bhandari

Devereaux

P. J.

Swiontkowski

M. F.

Schemitsch

E. H.

Shankardass

Sprague

Guyatt

G. H.

(2003). A randomized trial of opinion leader endorsement in a survey of orthopaedic surgeons: Effect on primary response rate. International Journal of Epidemiology, 32, 634–636.

12.

Bonevski

Magin

Horton

Foster

Girgis

(2011). Response rates in GP surveys: Trialling two recruitment strategies. Australian Family Physician, 40, 427–430.

13.

Bostick

R. M.

Pirie

Luepker

R. V.

Kofron

P. M.

(1992). Using physician caller follow-ups to improve the response rate to a physician telephone survey: Its impact and implications. Evaluation and the Health Professions, 15, 420–433.

14.

Burns

K. E. A.

Duffett

Kho

M. E.

Meade

M. O.

Adhikari

N. K. J.

Sinuff

Cook

D. J.

(2008). A guide for the design and conduct of self-administered surveys of clinicians. Canadian Medical Association Journal, 179, 245–252.

15.

Camuňas

Alward

R. R.

Vecchione

(1990). Survey response rates to a professional association mail questionnaire. Journal of the New York State Nurses Association, 21, 7–9.

16.

Cartwright

Ward

A. W.

(1968). Variations in general practitioners’ response to postal questionnaires. British Journal of Preventive and Social Medicine, 22, 199–205.

17.

Cook

J. V.

Dickinson

H. O.

Eccles

M. P.

(2009). Response rates in postal surveys of healthcare professionals between 1996 and 2005: An observational study. BMC Health Services Research, 9, 160.

18.

Cull

W. L.

O’Connor

K. G.

Sharp

Tang

S. S.

(2005). Response rates and response bias for 50 surveys of pediatricians. Health Services Research, 40, 213–226.

19.

Cummings

S. M.

Savitz

L. A.

Konrad

T. R.

(2001). Reported response rates to mailed physician questionnaires. Health Services Research, 35, 1347–1355.

20.

Curtis

E. A.

Redmond

R. A.

(2009). Survey postal questionnaire: Optimising response and dealing with non-response. Nursing Research, 16, 76–88.

21.

del Valle

Morgenstern

Rogstad

T. L.

Albright

Vickrey

B. G.

(1997). A randomized trial of the impact of certified mail on response rate to a physician survey, and a cost-effectiveness analysis. Evaluation and the Health Professions, 20, 389–406.

22.

Dillman

D. A.

Smyth

J. D.

Christian

L. M.

(2009). Internet, mail and mixed-mode surveys. The tailored design method. Hoboken, NJ: John Wiley.

23.

Donaldson

G. W.

Moinpour

C. M.

Bush

N. E.

Chapko

Jocom

Siadak

M.,

Sullivan

K. M.

(1999). Physician participation in research surveys: A randomized study of inducements to return mailed research questionnaires. Evaluation and the Health Professions, 22, 427–441.

24.

Dykema

Stevenson

Day

Sellers

S. L.

Bonham

V. L.

(2011). Effects of incentives and prenotification on response rates and costs in a national web survey of physicians. Evaluation and the Health Professions, 34, 434–447.

25.

Everett

S. A.

Price

J. H.

Bedell

A. W.

Telljohann

S. K.

(1997). The effect of a monetary incentive in increasing the return rate of a survey to family physicians. Evaluation and the Health Professions, 20, 207–214.

26.

Field

T. S.

Cadoret

C. A.

Brown

M. L.

Ford

Greene

S. M.

Hill

D.,

Zapka

J. M.

(2002). Surveying physicians: Do components of the “Total Design Approach” to optimizing survey response rates apply to physicians? Medical Care, 40, 596–606.

27.

Ford

Bammer

(2009). A research routine to assess bias introduced by low response rates in postal surveys. Nurse Researcher, 17, 44–53.

28.

Gattellari

Ward

J. E.

(2001). Will donations to their learned college increase surgeons’ participation in surveys? A randomized trial. Journal of Clinical Epidemiology, 54, 645–650.

29.

Gore-Felton

Koopman

Bridges

Thoresen

Spiegel

(2002). An example of maximizing survey return rates: Methodological issues for health professionals. Evaluation and the Health Professions, 25, 152–168.

30.

Grava-Gubins

Scott

(2008). Effects of various methodologic strategies: Survey response rates among Canadian physicians and physicians-in-training. Canadian Family Physician, 54, 1424–1430.

31.

Guise

Chambers

Valimaki

Makkonen

(2010). A mixed-mode approach to data collection: Combining web and paper questionnaires to examine nurses’ attitudes to mental illness. Journal of Advanced Nursing, 66, 1623–1632.

32.

Halpern

S. D.

Kohn

Dornbrand-Lo

Metkus

Asch

D. A.

Volpp

K. G.

(2011). Lottery-based versus fixed incentives to increase clinicians’ response to surveys. Health Services Research, 46, 1663–1674.

33.

Halpern

S. D.

Ubel

P. A.

Berlin

J. A.

Asch

D. A.

(2002). Randomized trial of $5 versus $10 monetary incentives, envelope size, and candy to increase physician response rates to mailed questionnaires. Medical Care, 40, 834–839.

34.

Hart

A. M.

Brennan

C. W.

Sym

Larson

(2009). The impact of personalized prenotification on response rates to an electronic survey. Western Journal of Nursing Research, 31, 17–23.

35.

Hawley

K. M.

Cook

J. R.

Jensen-Doss

(2009). Do noncontingent incentives increase survey response rates among mental health providers? A randomized trail comparison. Administration and Policy in Mental Health, 36, 343–348.

36.

Heywood

Mudge

Ring

Sanson-Fisher

(1995). Reducing systematic bias in studies of general practitioners: The use of a medical peer in the recruitment of general practitioners in research. Family Practice, 12, 227–231.

37.

Hill

C. A.

Fahrney

Wheeless

S. C.

Carson

C. P.

(2006). Survey response inducements for registered nurses. Western Journal of Nursing Research, 28, 322–334.

38.

James

K. M.

Ziegenfuss

J. Y.

Tilburt

J. C.

Harris

A. M.

Beebe

T. J.

(2011). Getting physicians to respond: The impact of incentive type and timing on physician survey response rates. Health Services Research, 46, 232–242.

39.

Johnson

T. P.

Wislar

J. S.

(2012). Response rates and nonresponse errors in surveys. Journal of the American Medical Association, 307, 1805–1806.

40.

Kanaan

R. A. A.

Wessely

S. C.

Armstrong

(2010). Differential effects of pre and post-payment on neurologists’ response rates to a postal survey. BMC Neurology, 10, 100.

41.

Keating

N. L.

Zaslavsky

A. M.

Goldstein

West

D. W.

Ayanian

J. Z.

(2008). Randomized trial of $20 versus $50 incentives to increase physician survey response rates. Medical Care, 46, 878–881.

42.

Kellerman

S. E.

Herold

(2001). Physician response to surveys: A review of the literature. American Journal of Preventive Medicine, 20, 61–67.

43.

Kephart

W. M.

Bressler

(1958). Increasing the responses to mail questionnaires. Public Opinion Quarterly, 22, 123–132.

44.

Klabunde

C. N.

Willis

G. B.

McLeod

C. C.

Dillman

D. A.

Johnson

T. P.

Greene

S. M.

Brown

M. L.

(2012). Improving the quality of surveys of physicians and medical groups: A research agenda. Evaluation and the Health Professions, 35, 477–506.

45.

Lusk

Delclos

G. L.

Burau

Drawhorn

D. D.

Aday

L. A.

(2007). Mail versus internet surveys: Determinants of method of response preferences among health professionals. Evaluation and the Health Professions, 30, 186–201.

46.

Malin

J. L.

Rideout

Ganz

P. A.

(2000). Tracking managed care: The importance of a cash incentive for medical director response to a survey. American Journal of Managed Care, 6, 1209–1214.

47.

Martins

Lederman

R. I.

Lowenstein

C. I.

Joffe

Neville

B. A.

Hastings

B. T.

Abel

G. A.

(2012). Increasing response rates from physicians in oncology research: A structured literature review and data from a recent physician survey. British Journal of Cancer, 106, 1021–1026.

48.

Matteson

K. A.

Anderson

B. L.

Pinto

S. B.

Lopes

Schulkin

Clark

M. A.

(2011). Surveying ourselves: Examining the use of a web-based approach for a physician survey. Evaluation and the Health Professions, 34, 448–463.

49.

McLaren

Shelley

(2000). Response rates of Victorian general practitioners to a mailed survey on miscarriage: Randomised trial of a prize and two forms of introduction to the research. Australian and New Zealand Journal of Public Health, 24, 360–364.

50.

McLeod

C. C.

Klabunde

C. N.

Willis

G. B.

Stark

(2013). Health care provider surveys in the United States, 2000-2010: A review. Evaluation and the Health Professions, 36, 106–126.

51.

McFarlane

Olmsted

M. G.

Murphy

Hill

C. A.

(2006). Nonresponse bias in a mail survey of physicians. Paper presented at the Annual Conference of the American Association for Public Opinion Research, Montreal, Canada.

52.

Myerson

(1993). Improving the response rates in primary care research. Some methods used in a survey on stress in general practice since the new contract (1990). Family Practice, 10, 342–346.

53.

Nicholls

Chapman

Shaw

Perkins

Sullivan

M. M.

Crutchfield

Reed

(2011). Increasing response rates in physician surveys: The limited utility of electronic options. Health Services Research, 46, 1675–1682.

54.

Olson

(2006). Survey participation, nonresponse bias, measurement error bias, and total bias. Public Opinion Quarterly, 70, 737–758.

55.

Olson

Schneiderman

Armstrong

R. V.

(1993). Increasing physician survey response rates without biasing survey results. Proceedings of the Section on Survey Research Methods of the American Statistical Association, Alexandria, VA, pp. 1036–1041.

56.

Parsons

J. A.

Warnecke

R. B.

Czaja

R. F.

Barnsley

Kaluzny

(1994). Factors associated with response rates in a national survey of primary care physicians. Evaluation Review, 18, 756–766.

57.

Paul

C. L.

Walsh

R. A.

Tzelepis

(2005). A monetary incentive increases postal survey response rates for pharmacists. Journal of Epidemiology and Community Health, 59, 1099–1101.

58.

Puleo

Zapka

White

M. J.

Mouchawar

Somkin

Taplin

(2002). Caffeine, cajoling, and other strategies to maximize clinician survey response rates. Evaluation and the Health Professions, 25, 169–184.

59.

Raudenbush

S. W.

Bryk

A. S.

(2002). Hierarchical linear models (2nd ed.). Thousand Oaks, CA: Sage.

60.

Raudenbush

S. W.

Bryk

A. S.

Cheong

A. S.

Fai

Y. F.

Congdon

R. T.

du Toit

(2011). HLM 7: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.

61.

Recklitis

C. J.

Campbell

E. G.

Kutner

J. S.

Bober

S. L.

(2009). Money talks: Non-monetary incentive and Internet administration fail to increase response rates to a physician survey. Journal of Clinical Epidemiology, 62, 224–226.

62.

Scott

Jeon

S. H.

Joyce

C. M.

Humphreys

J. S.

Kalb

Witt

Leahy

(2011). A randomised trial and economic evaluation of the effect of response mode on response rate, response bias, and item non-response in a survey of doctors. BMC Medical Research Methodology, 11, 26.

63.

Singer

Couper

M. P.

(2008). Do incentives exert undue influence on survey participation? Experimental evidence. Journal of Empirical Research on Human Research Ethics, 3, 49–56.

64.

Temple-Smith

Mulvey

Doyle

(1998). Maximizing response rates in a survey of general practitioners: Lessons from a Victorian survey on sexually transmissible diseases. Australian Family Physician, 27, S15–S18.

65.

Templeton

Deehan

Taylor

Drummond

Strang

(1997). Surveying general practitioners: Does a low response rate matter? British Journal of General Practice, 47, 91–94.

66.

Thorpe

Ryan

McLean

S. L.

Burt

Stewart

Brown

J. B.

Reid

G. J.

Harris

(2008). How to obtain excellent response rates when surveying physicians. Family Practice, 26, 65–68.

67.

Ulrich

C. M.

Danis

Koziol

Garrett-Mayer

Hubbard

Grady

(2005). Does it pay to play? A randomized trial of prepaid financial incentives and lottery incentives in surveys of nonphysician healthcare professionals. Nursing Research, 54, 178–183.

68.

Ulrich

C. M.

Grady

(2004). Financial incentives and response rates in nursing research. Nursing Research, 53, 73–74.

69.

VanGeest

J. B.

Johnson

T. P.

(2011). Surveying nurses: Identifying strategies to improve participation. Evaluation and the Health Professions, 34, 487–511.

70.

VanGeest

J. B.

Johnson

T. P.

Welch

V. L.

(2007). Methodologies for improving response rates in surveys of physicians: A systematic review. Evaluation and the Health Professions, 30, 303–321.

71.

VanGeest

J. B.

Wynia

M. K.

Cummins

D. S.

Wilson

I. B.

(2001). Effects of different monetary incentives on the return rate of a national survey of physicians. Medical Care, 39, 197–201.

72.

Viera

A. J.

Edwards

(2012). Does an offer for a free on-line continuing medical education (CME) activity increase physician survey response rate? A randomized trial. BMC Research Notes, 5, 129.

73.

VonRiesen

R. D.

(1979). Postcard reminders versus replacement questionnaires and mail survey response rates from a professional population. Journal of Business Research, 7, 1–7.

74.

Wilson

P. M.

Petticrew

Calnan

Nazareth

(2010). Effects of a financial incentive on health researchers’ response to an online survey: A randomized controlled trial. Journal of Medical Internet Research, 12, e13. doi:10.2196/jmir.1251

75.

Ziegenfuss

J. Y.

Burmeister

James

K. M.

Haas

Tilburt

J. C.

Beebe

T. J.

(2012). Getting physicians to open the survey: Little evidence that an envelope teaser increases response rates. BMC Medical Research Methodology, 12, 41. doi:10.1186/1471-2288-12-41