Factors Influencing the Return Rates in Mail Surveys : Effects of Paper Size, Number of Pages, and Cover Letter Appeals

郵送調査の返送率に影響を及ぼす要因を究明するため3つ研究が行われた。第1の研究では、片面刷、2ページ見開き、2枚から成る2種類のサイズの質問紙が返送率に及ぼす効果を検討した。一方の質問紙はA3判（297×420mm）の用紙に、他方の質問紙はB4判（257×364mm）の用紙に印刷され、それぞれ353名の調査対象者に郵送された。返送率は60.5％（A3）と55.0％（B4）で、この差は統計的に有意でなかった。第2の研究では、質問紙の枚数が返送率に及ぼす効果を検討した。質問紙は、B5判（182×257mm）の用紙に片面印刷された6枚のものとB4判の用紙に見開き2ページで片面印刷された3枚のものであった。B5判の質問紙は308名の調査対象者に、B4判の質問紙は307名の調査対象者に郵送された。B5判の質問紙に対する返送率（62.1％）は、B4判の質問紙に対する返送率（56.1％）と有意差がなかった。第3の研究では、4種類の協力依頼状の要請表現が返送率に及ぼす効果を調べた｡第1条件は標準的な協力依頼状、第2条件は、前回の調査結果がマスコミに取り上げられたことを付記した協力依頼状、第3条件は、調査報告書を送呈することを付記した協力依頼状､第4条件は､第2と第3の条件を併記した協力依頼状であった。4条件の返送率（60.7％、55.7％、61.0％、55.8％）に有意な差がなかった。Three studies investigated factors that can influence return rates in mail surveys. The first study examined the effect of two sizes of paper using a questionnaire consisting of two single-sided pages with two columns of questions per page. One version was printed on A3 (297 x 420mm) paper and mailed to 353 persons, A second version was on B4 (257 x 364mm) paper and mailed to 353 persons. The return rates were 60.5% (A3) and 55.0% (B4) but this difference was not statistically significant. In the second study the effect of number of pages was examined. The questionnaire was either printed on 6 single-sided B5 (182 x 257mm) sheets or 3 single-sided, two column B4 sheets. 308 B5 and 307 B4 questionnaires were mailed. The return rate for B5 (62.1%) was not statistically different than that for B4 (56.1%) . The third study investigated the effect of four cover letter appeals. The first condition consisted of a standard cover letter. The second was the same as the first with the mention that the results of previous study were discussed in the media. The third was the same as the first with an offer of a copy of the research report. The fourth was the standard cover letter with both additions of the second and third conditions. The return rates of the four conditions (60.7%, 55.7%, 61.0%, 55.8%) were statistically equal

Revisiting some earlier thoughts

Non UBCUnreviewedFacult

A Robotic Approach to Understanding Robustness

Robustness is a property present in every living system which provides resilience against internal or external perturbations. Robustness is also highly desirable in engineered systems, as it makes them more resistant to unpredicted events. Despite its ubiquity, this concept is not yet understood and no existing framework provides a methodology to quantify it. Our work presents an approach to this problem through the use of robots acting as models for the study of robust organisms. Using robots, we look at how a change of robustness in sub-systems influences the robustness of the whole system. Our results show that using robotics offers an adequate level of complexity to study robustness while providing enough control to improve our understanding of this concept

A Robotic Approach to Understanding the Role and the Mechanism of Vicarious Trial-And-Error in a T-Maze Task

<div><p>Vicarious trial-and-error (VTE) is a behavior observed in rat experiments that seems to suggest self-conflict. This behavior is seen mainly when the rats are uncertain about making a decision. The presence of VTE is regarded as an indicator of a deliberative decision-making process, that is, searching, predicting, and evaluating outcomes. This process is slower than automated decision-making processes, such as reflex or habituation, but it allows for flexible and ongoing control of behavior. In this study, we propose for the first time a robotic model of VTE to see if VTE can emerge just from a body-environment interaction and to show the underlying mechanism responsible for the observation of VTE and the advantages provided by it. We tried several robots with different parameters, and we have found that they showed three different types of VTE: high numbers of VTE at the beginning of learning, decreasing numbers afterward (similar VTE pattern to experiments with rats), low during the whole learning period, and high numbers all the time. Therefore, we were able to reproduce the phenomenon of VTE in a model robot using only a simple dynamical neural network with Hebbian learning, which suggests that VTE is an emergent property of a plastic and embodied neural network. From a comparison of the three types of VTE, we demonstrated that 1) VTE is associated with chaotic activity of neurons in our model and 2) VTE-showing robots were robust to environmental perturbations. We suggest that the instability of neuronal activity found in VTE allows ongoing learning to rebuild its strategy continuously, which creates robust behavior. Based on these results, we suggest that VTE is caused by a similar mechanism in biology and leads to robust decision making in an analogous way.</p></div

Success rates with longer distance from the tactile cue to the reward.

<p>The axis shows the length to the goal, while the <i>y</i> axis indicates the success rates. HL: High to low VTE. L: Low VTE. H: High VTE. Minimal: Minimal model.</p

T-maze environment used for the experiment.

<p>At the beginning of each trial, the robot was placed on the central arm of the maze. The initial position of the center of the body was originally set to . The circle at the choice point represents the tactile cue, the star at one end of the maze indicates reward, and the lightning at the other end of the maze stands for punishment. All the sizes and distances are in centimeters, which is scaled based on actual e-puck robots.</p

Maximum Lyapunov exponents (MLE) averaged over the 100 trials.

<p>The MLE is calculated for each five modules, and for each evolved 16 robots, which is denoted by HL1, HL2, …, Mimimal2. The time series used for estimation the MLE is an averaged neural activity for the respective modules. The error bar indicates standard deviation.</p

The number of VTEs for the evolved 16 robots and a comparison of the performance with and without learning.

<p>Each figure represents the result from an individual robot. The <i>x</i> axis denotes trials and has auxiliary lines every 10 trials. The scale of each axis is set to the same value in every figure. Red line: Success rate when the robot ran the trial with learning on. Green line: Success rate when the robot replied the trial with learning off. Black line: Number of VTEs observed in the original condition (i.e., with learning activated and from the original starting point; the same condition as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102708#pone-0102708-g003" target="_blank">Figure 3</a>) HL: High to low VTE. L: Low VTE. H: High VTE. Minimal: Minimal model.</p