Through its integrated approach to quantitative research methods, this text teaches readers how to plan, conduct, and write a research project and select and interpret data so they can become better consumers of research. This is not a statistics book-there are very few formulas. Rather, this book helps students master which statistic to use when and how to interpret the results. Organized around the steps one takes in conducting a research project, this book is ideal for applied programs and for those who want to analyze and evaluate research articles. Having taught in a variety of departments, the authors have a good grasp of the research problems faced by master's and doctoral students in diverse areas of the behavioral and social sciences. Text adopters applaud the book's clarity. Students are often confused by other texts' use of inconsistent terminology. To avoid this confusion, the authors present a semantically consistent picture that emphasizes five research approaches-- randomized experimental, quasi-experimental, comparative, associational, and descriptive. The authors then show how these approaches lead to three kinds of research designs which, in turn, lead to three groups of statistics with the same names. This consistent framework increases comprehension and the ability to apply the material. Numerous applied problems, annotated examples, and diagrams and tables further promote comprehension. Although the book emphasizes quantitative research, the value of qualitative research is introduced. This extensively revised edition features more than 50% new material including: A new chapter on the evidence-based approach that emphasizes the importance of reporting confidence intervals and effect sizes and the increased use of meta-analysis. An increased emphasis on evaluating research including an 8 step plan for evaluating research validity (Chs. 23 & 24) and its application to the 5 sample studies used throughout the book (Ch. 25). Lots of practical advice on planning a research project (Ch. 2), data collection and coding (Ch. 15), writing the research report (Ch. 27), questions to use in evaluating a research article (Appendix E) and creating APA tables and figures (Appendix F). A new chapter on non-experimental approaches/designs (Ch. 7) including qualitative research. Web resources for students including critical thinking problems with answers and a sample outline of a research proposal. An earlier and expanded introduction to measurement reliability and validity to further emphasize their differences and importance. An extensively revised chapter on measurement validity consistent with the latest APA/AERA/NCME standards. Fewer chapters on inferential statistics with an increased focus on how their selection is related to the design of the study and how to interpret the results using significance testing and effect sizes and confidence intervals. Instructor's Resources with Power Points, test questions, answers to the application questions, and more. Intended for graduate research or quantitative/experimental methods/design courses in psychology, education, human development and family studies, and other behavioral, social, business, and health sciences, independent sections and chapters can be read in many orders allowing for flexibility in assigning topics. Due to its practical approach, this book also appeals to researchers and clinicians. Prior exposure to statistics and research methods is recommended.
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这本书的叙事风格异常沉闷,阅读起来就像是在啃一块未经调味的干面包,尽管你知道它可能营养丰富,但吞咽的过程却充满了折磨。作者似乎认为,学术的严肃性必须通过冗长、被动语态和绕口的句子来体现。很多本可以直截了当地用几句话说明白的原则,在书中却被分解成冗长复杂的段落,充斥着大量的定语从句和技术行话,让人不得不反复重读才能勉强理解其核心意思。例如,描述一个简单的实验预设时,作者能够用上三段话来铺垫其理论基础,而真正关键的“如何操作”却被轻描淡写地放在脚注里。这对于那些需要快速学习和应用方法的研究者来说,无疑是巨大的时间消耗。我更欣赏那些能够用清晰、富有洞察力的语言,将复杂概念简单化的作者,他们尊重读者的智商和时间。这本书显然没有采纳这种现代化的沟通策略,它的文字更像是为同行评审者准备的辩护词,而不是为渴望学习和实践的研究生准备的学习材料。
评分这本书最大的缺陷,可能在于其对“应用”的理解过于狭隘和教条化。它似乎预设了一个理想化的、资源充足的研究机构环境,在那里,研究者可以轻易获得昂贵的软件授权、专业的统计咨询,并且可以设计出完美平衡的控制组和实验组。然而,现实世界中的“应用设置”,尤其是在非营利组织、小型企业或发展中国家的研究背景下,往往是关于如何“凑合”出有效数据,如何在强烈的政治干预下坚持方法论的严谨性。这本书在讨论“样本代表性”时,只是机械地强调随机抽样是金标准,却几乎没有提供如何在大范围的、非概率抽样场景下,通过严谨的权重调整和敏感性分析来提高结果推论的可靠性。这种对现实限制的视而不见,使得书中的方法论指导在很大程度上失去了其声称的“应用价值”。它更像是一本纯粹的方法论理论导论,而非一本真正能指导研究者在复杂泥泞的实际工作中披荆斩棘的实战手册。
评分这本号称“应用环境研究方法”的书,实在让人摸不着头脑,与其书名所承诺的“实践指导”相去甚远。初翻几页,我就感到一种强烈的脱节感。作者似乎沉迷于高深的理论框架和晦涩的术语堆砌,却完全忽略了实际操作中研究者会遇到的那些鸡毛蒜皮的困境。比如,关于数据收集环节的描述,简直是教科书式的理想化,完全没有提及如何处理小型机构中资源匮乏、受访者配合度低下的现实问题。我期待看到的是一套可以在真实世界中立即施展的工具箱,而不是一堆只能在象牙塔里供人赏玩的模型。特别是关于定性研究的章节,作者花费了大量篇幅讨论“解释学的深度转向”,却对如何进行有效的焦点小组访谈、如何安抚不愿透露信息的关键知情人等实操技巧只是一笔带过,甚至有些避重就轻。这让我不禁怀疑,作者是否真的深入到任何“应用场景”中去进行过真正的田野调查?读完后,我感觉自己掌握了一些新的哲学名词,但面对下一个需要设计调查问卷或招募实验参与者的任务时,我依然感到手足无措,这本书提供的帮助微乎其微,更像是一次理论上的巡礼,而非实战的训练。
评分我对这本书的结构安排感到非常困惑,它似乎试图涵盖太多领域,结果却是样样稀松。从方法论的哲学基础到具体的统计软件操作指南,作者像一个急于展示自己知识广度的学生,把所有能想到的内容都塞进了有限的篇幅里。这种“大而全”的做法,导致在每一个关键的实践点上,深度都严重不足。例如,当我们谈及因果推断时,作者只是罗列了各种设计(RCT, QAI等),但对于如何在资源受限的社会科学研究中,选择出最符合伦理和可行性的设计,书中几乎没有提供任何决策树或案例分析。阅读体验非常碎片化,仿佛是把好几本不同水平教材的章节硬生生地拼凑在一起。我花了大量时间去辨认哪些部分是作者原创的深刻见解,哪些是直接引用或照搬了其他经典著作的陈词滥调。这种缺乏统一核心焦点的写作方式,使得读者很难建立起一个连贯的、可供记忆和复用的方法论知识体系。最终,这本书给我的印象是一个庞杂的知识清单,而非一个清晰的实践蓝图。
评分关于伦理考量的部分,我发现这本书的处理方式显得过于保守和脱离现实,甚至有些脱节于当前快速发展的研究环境。作者详细阐述了基本的知情同意和保密原则,这些都是基础中的基础,任何入门教材都会涵盖。然而,在面对大数据、社交媒体挖掘以及跨文化合作中出现的新型伦理困境时,书中提供的指导却显得苍白无力。例如,当涉及到对网络公开数据的抓取和分析时,书中对“隐私边界”的定义依然停留在上世纪末期的纸质档案阶段,完全没有触及算法偏见、二次使用数据的责任划分等现代研究者必须面对的棘手问题。这让我感到非常失望,因为“应用环境”意味着研究必须与时俱进,必须解决当下正在发生的问题,而不是沉溺于对过去规范的重复强调。这本书似乎错过了捕捉研究实践前沿动态的机会,提供的伦理指南更像是一种形式上的合规清单,而非真正具有前瞻性和指导意义的智慧结晶。
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