بایسته های استفاده از ابزار پرسش نامه در پایان نامه های رشته هنر (مطالعه موردی: پایان نامه های کارشناسی ارشد طراحی صنعتی) (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
استفاده از ابزار پرسش نامه یکی از متداول ترین روش های جمع آوری اطلاعات در پایان نامه های رشته های هنر است. همچنین بسیاری از یافته های پایان نامه های دانشجویان رشته طراحی صنعتی بر پایه داده های بدست آمده از ابزار پر اهمیت پرسش نامه می باشد. ماهیت ذهنی و تجربی هنر، تنوع در موضوعات مورد مطالعه و نبود پرسش نامه های استاندارد در موضوعات مختلف این حوزه، تنها گوشه ای از چالش های موجود در استفاده از ابزار پرسش نامه به شمار می رود. این پژوهش با هدف بررسی خطاهای رایج در به کارگیری پرسش نامه توسط دانشجویان طراحی صنعتی و ارائه نکات کلیدی در طراحی و استفاده از آن، به دو پرسش اساسی پاسخ می دهد: دانشجویان طراحی صنعتی در استفاده از پرسش نامه ها چه خطاهایی را مرتکب می شوند؟ در طراحی و به کارگیری پرسش نامه، چه نکاتی باید مورد توجه قرار گیرد؟ برای رسیدن به پاسخ این پرسش ها، ابتدا با استفاده از روش نمونه گیری هدفمند، 30 پایان نامه کارشناسی ارشد رشته طراحی صنعتی به عنوان نمونه مورد بررسی قرار گرفت. سپس، با به کارگیری روش تحلیل محتوای کیفی، مهم ترین خطاهای موجود در پرسش نامه این پایان نامه ها استخراج، کدگذاری، مقوله بندی و تفسیر شدند. یافته های این پژوهش نشان داد که پرسش نامه ها عمدتاً در سه سطح محتوای پرسش نامه، طراحی سوال ها و گزینه های پاسخ و ارائه پرسش نامه، دارای خطاهای راهبردی هستند. در نهایت در بخش نتیجه گیری مجموعه ای از دستورالعمل ها به منظور طراحی و استفاده از پرسش نامه های کاربردی مناسب برای پژوهش های طراحی صنعتی و سایر رشته های هنری ارائه گردیده است.Essential Considerations for Using Questionnaires in Art Theses (Case Study: Master's Theses in Industrial Design)
Problem Statement and Research Questions: Questionnaires, due to their ease of use, affordability, and accessibility, are one of the most common methods of data collection in art research. In the field of industrial design, a cursory glance reveals that questionnaires form the basis of many findings in theses and dissertations of students in this field, used to collect both quantitative and qualitative data from a wide range of audiences. Despite the numerous advantages of questionnaires, their use in art research, particularly in the field of industrial design, is accompanied by unique challenges. The diversity of audiences and subjects of study in art research, the lack of standardized questionnaires in various topics in this field, the lack of attention to the validity, reliability, and credibility of these questionnaires, and the subjective and experiential nature of art, which makes artistic phenomena not fully measurable with quantitative tools such as questionnaires, are some of the challenges in using questionnaires. Therefore, the main questions of this study are as follows:
What common mistakes do industrial design students make when using questionnaires?
What points should be considered when designing and using a questionnaire?
Addressing these questions is crucial to optimizing research efficiency and ensuring the validity of our findings. This study aims to critically evaluate the use of questionnaires in a sample of industrial design master's theses, where questionnaires have been employed as the primary data collection method.
Research Method: This applied research employs qualitative content analysis to interpret the phenomenon under study. The main stages of this method include coding, categorization, and interpretation (Ghaedi & Golshani, 2016). The data collection method in this research is library-based, and the statistical population consists of 30 master's theses in industrial design from 2012 to 2022, which were selected through purposive sampling. These theses have used questionnaires as their primary or secondary data collection tool. For data analysis, in the initial coding stage, the text, questions, and each section of the questionnaires were carefully examined, categorized, and a code was assigned to each identified error. Subsequently, in the categorization stage, similar codes were organized into related groups (categories). Finally, in the interpretation section, each category and identified error was interpreted.
Results: The initial phase of the analysis focused on identifying errors within the questionnaires. A comprehensive review revealed 57 distinct errors, which were subsequently categorized and consolidated into 22 primary error types. To provide a more granular understanding of these errors, a hierarchical categorization was employed, dividing the errors into three levels based on their nature: questionnaire content, question and response option design, and questionnaire administration. At the first level, addressing content-related issues within the questionnaire, the most prevalent errors included: content inconsistencies, overlapping content, and a mismatch between intended results and actual content. The second level focused on the design of questions and response options. Here, common errors were: a neglect of qualitative data, misuse of question types, double-barreled questions, lack of logical sequencing, inconsistent numbers of response options, restricted response scales, ambiguous question wording, leading questions, and an imbalance between open-ended and closed-ended questions. The third level examined the overall presentation of the questionnaire. Key issues identified included: a lack of clear instructions and information, inappropriate demographic questions, inconsistent tone and clarity in the wording, overly complex and lengthy questions, the use of technical jargon unfamiliar to respondents, grammatical errors, inadequate spacing between questions and answers, unsuitable images, and insufficient space for responses.
Conclusion: The identified errors and their subsequent analysis have paved the way for the development of a set of guidelines across the three aforementioned levels, aimed at designing effective questionnaires. At the first level, addressing content-related issues within the questionnaire, the guidelines include the following:
Clearly defining the research objectives and ensuring that all questions are directly aligned with these objectives.
Avoiding abrupt topic shifts and organizing questions in a logical sequence to create a coherent flow.
Thoroughly reviewing the questionnaire to eliminate unnecessary questions and retain only those that are essential.
Ensuring that response options are clear, concise, and directly related to the question, avoiding redundancy.
Reviewing and refining questions before conducting data analysis.
Conducting a rigorous analysis of the findings, interpreting responses objectively, and comparing them to existing evidence.
Employing appropriate statistical methods and providing objective interpretations of the results.
Utilizing validated tools to ensure the reliability of the results and providing a comprehensive interpretation of the findings.
The second level pertains to the design of questions and response options, where the following considerations should be taken into account:
Identifying the type of data and using appropriate methods for analyzing qualitative data. (This means determining whether you're collecting numerical or textual data and using the correct analysis techniques for each.)
Designing questions that align with the chosen data collection method. (Ensuring that the questions are suitable for the chosen research method, such as interviews, surveys, or observations.)
Training interviewers on how to ask open-ended questions, take detailed notes, and accurately record responses.
Employing suitable qualitative data analysis techniques such as coding, content analysis, and thematic analysis.
Using Likert scales only when responses fall within a specific range or continuum.
Using multiple-choice questions only when there are a limited number of predefined response options.
Using ranking questions only when responses can be ordered or ranked.
Using open-ended questions when seeking detailed and in-depth information.
Using a balanced mix of question types to gather a comprehensive dataset.
Framing each question individually and stating its purpose.
Designing questions to minimize invalid, random, or incorrect responses.
Arranging questions in a logical and systematic order, grouped by topic and research objectives.
Progressing from simple to more complex questions.
Using a consistent number of response options for similar questions.
Providing enough response options to allow for a complete and accurate answer.
Limiting the number of response options to avoid overwhelming respondents.
Using "all of the above," "none of the above," and "other" options when appropriate.
Ensuring that response constraints (e.g., minimum, maximum, required) align with the question type (open-ended, closed-ended).
Using clear, simple, and understandable language, avoiding ambiguous or misleading information.
Using neutral and unbiased language, avoiding leading questions or judgmental statements.
Avoiding images or diagrams that suggest a particular response.
Balancing the number of open-ended questions based on the research topic and objectives, and designing them to be clear and guiding.
Using a mix of open-ended and closed-ended questions to gather a diverse range of data.
The following guidelines should be considered when presenting a questionnaire:
Clearly stating the purpose of the questionnaire at the beginning.
Providing clear and concise instructions for answering each question.
Maintaining a connection between demographic information and the questionnaire's objectives.
Ensure that the demographic questions collected are relevant to the research goals.
Balancing the number of demographic questions with the questionnaire's needs.
Avoid overwhelming respondents with unnecessary demographic questions.
Using a polite, neutral, and consistent tone throughout the questionnaire.
Using positive phrasing in a balanced manner.
Avoid leading questions or making assumptions about the respondent's viewpoint.
Providing neutral or negative options to accurately reflect opinions.
Ensure that respondents have the option to express disagreement or neutral feelings.
Using short, simple sentences in questions.
Breaking down complex questions into simpler, more understandable parts.
Using simple and understandable language for the target audience.
Avoid jargon or technical terms that the respondents may not understand.
Providing clear definitions for any technical terms.
Proofreading the questionnaire for spelling errors.
Correcting any grammatical mistakes.
Providing clear and separate spaces for questions and answer options.
Using a layout that prevents confusion between questions and answers.
Using high-quality images that are relevant to the questionnaire's topic.
Visuals can enhance understanding and engagement.
Too many images can be distracting.
Ensuring a clear connection between images and the questionnaire content.
Images should support the questions and not be misleading.
Providing adequate space for open-ended responses.
Setting character limits for written responses when necessary.
Neglecting the aforementioned points and committing similar errors to those identified in this research can render the results of a questionnaire ineffective and lead research down the wrong path. Therefore, the proposed solutions can serve as guidelines for researchers in the field of design and other art-related disciplines. However, achieving a complete and comprehensive model for questionnaire design still requires further and more in-depth studies in this area.
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