Stephen Krashen's Theory of Second Language Acquisition

資料來源 http://www.sk.com.br/sk-krash.html

"Language acquisition does not require extensive use of conscious grammatical rules, and does not require tedious drill." Stephen Krashen

"Acquisition requires meaningful interaction in the target language - natural communication - in which speakers are concerned not with the form of their utterances but with the messages they are conveying and understanding." Stephen Krashen

"The best methods are therefore those that supply 'comprehensible input' in low anxiety situations, containing messages that students really want to hear. These methods do not force early production in the second language, but allow students to produce when they are 'ready', recognizing that improvement comes from supplying communicative and comprehensible input, and not from forcing and correcting production." Stephen Krashen

"In the real world, conversations with sympathetic native speakers who are willing to help the acquirer understand are very helpful." Stephen Krashen

Introduction

Stephen Krashen (University of Southern California) is an expert in the field of linguistics, specializing in theories of language acquisition and development. Much of his recent research has involved the study of non-English and bilingual language acquisition. During the past 20 years, he has published well over 100 books and articles and has been invited to deliver over 300 lectures at universities throughout the United States and Canada.

This is a brief description of Krashen's widely known and well accepted theory of second language acquisition, which has had a large impact in all areas of second language research and teaching since the 1980s.

Description of Krashen's Theory of Second Language Acquisition

Krashen's theory of second language acquisition consists of five main hypotheses:

the Acquisition-Learning hypothesis,
the Monitor hypothesis,
the Natural Order hypothesis,
the Input hypothesis,
and the Affective Filter hypothesis.
The Acquisition-Learning distinction is the most fundamental of all the hypotheses in Krashen's theory and the most widely known among linguists and language practitioners.
According to Krashen there are two independent systems of second language performance: 'the acquired system' and 'the learned system'. The 'acquired system' or 'acquisition' is the product of a subconscious process very similar to the process children undergo when they acquire their first language. It requires meaningful interaction in the target language - natural communication - in which speakers are concentrated not in the form of their utterances, but in the communicative act.

The 'learned system' or 'learning' is the product of formal instruction and it comprises a conscious process which results in conscious knowledge 'about' the language, for example knowledge of grammar rules. According to Krashen 'learning' is less important than 'acquisition'. (Veja o texto ao lado e também outra página em português sobre Acquisition/Learning).

The Monitor hypothesis explains the relationship between acquisition and learning and defines the influence of the latter on the former. The monitoring function is the practical result of the learned grammar. According to Krashen, the acquisition system is the utterance initiator, while the learning system performs the role of the 'monitor' or the 'editor'. The 'monitor' acts in a planning, editing and correcting function when three specific conditions are met: that is, the second language learner has sufficient time at his/her disposal, he/she focuses on form or thinks about correctness, and he/she knows the rule.

It appears that the role of conscious learning is somewhat limited in second language performance. According to Krashen, the role of the monitor is - or should be - minor, being used only to correct deviations from 'normal' speech and to give speech a more 'polished' appearance.

Krashen also suggests that there is individual variation among language learners with regard to 'monitor' use. He distinguishes those learners that use the 'monitor' all the time (over-users); those learners who have not learned or who prefer not to use their conscious knowledge (under-users); and those learners that use the 'monitor' appropriately (optimal users). An evaluation of the person's psychological profile can help to determine to what group they belong. Usually extroverts are under-users, while introverts and perfectionists are over-users. Lack of self-confidence is frequently related to the over-use of the 'monitor'.
AS DUAS HIPÓTESES MAIS IMPORTANTES DA TEORIA DE KRASHEN, E SUA INTERRELAÇÃO

A hipótese acquisition-learning e a hipótese monitor representam a essência da teoria de Krashen.

De acordo com sua teoria, acquisition é responsável pelo entendimento e pela capacidade de comunicação criativa: habilidades desenvolvidas subconscientemente. Isto ocorre através da familiarização com a característica fonética da língua, sua estruturação de frases, seu vocabulário, tudo decorrente de situações reais, bem como pela descoberta e assimilação de diferenças culturais e pela aceitação e adaptação à nova cultura.

Learning depende de esforço intelectual e procura produzir conhecimento consciente a respeito da estrutura da língua e de suas irregularidades, e preconiza a memorização de vocabulário fora de situações reais. Este conhecimento atua na função de monitoramento da fala. Entretanto, o efeito deste monitoramento sobre a performance da pessoa, depende muito do perfil psicológico de cada um.

Veja aqui mais sobre os conceitos de acquisition e learning.

A hipótese monitor explica a relação entre acquisition e learning ao definir a influência deste último sobre o primeiro. Os esforços espontâneos e criativos de comunicação, decorrentes de nossa capacidade natural de assimilar línguas quando em contato com elas, são policiados e disciplinados pelo conhecimento consciente das regras gramaticais da língua e de suas exceções.

Os efeitos deste monitoramento sobre pessoas com diferentes características de personalidade serão vários. Pessoas que tendem à introversão, à falta de autoconfiança, ou ao perfeccionismo, pouco se beneficiarão de um conhecimento da estrutura da língua e de suas irregularidades. Pelo contrário, no caso de línguas com alto grau de irregularidade (como o inglês), poderão desenvolver um bloqueio que compromete a espontaneidade devido à consciência da alta probabilidade de cometerem erros.

Pessoas que tendem à extroversão, a falar muito, de forma espontânea e impensada, também pouco se beneficiarão de learning, uma vez que a função de monitoramento é quase inoperante, está submetida a uma personalidade intempestiva que se manifesta sem maior cautela. Os únicos que se beneficiam de learning, são as pessoas mais normais e equilibradas, que sabem aplicar a função de monitoramento de forma moderada. Mesmo assim, numa situação real de comunicação, o monitoramento só funcionará se ocorrerem 3 condições simultaneamente:

- Tempo suficiente: que a pessoa disponha de tempo suficiente para avaliar as alternativas com base nas regras incidentes.
- Preocupação com a forma: que a pessoa concentre atenção não apenas no ato da comunicação, no conteúdo da mensagem, mas também e principalmente na forma.
- Conhecimento da regra: que a pessoa tenha conhecimento da regra que se aplica ao caso.


The Natural Order hypothesis is based on research findings (Dulay & Burt, 1974; Fathman, 1975; Makino, 1980 cited in Krashen, 1987) which suggested that the acquisition of grammatical structures follows a 'natural order' which is predictable. For a given language, some grammatical structures tend to be acquired early while others late. This order seemed to be independent of the learners' age, L1 background, conditions of exposure, and although the agreement between individual acquirers was not always 100% in the studies, there were statistically significant similarities that reinforced the existence of a Natural Order of language acquisition. Krashen however points out that the implication of the natural order hypothesis is not that a language program syllabus should be based on the order found in the studies. In fact, he rejects grammatical sequencing when the goal is language acquisition.

The Input hypothesis is Krashen's attempt to explain how the learner acquires a second language. In other words, this hypothesis is Krashen's explanation of how second language acquisition takes place. So, the Input hypothesis is only concerned with 'acquisition', not 'learning'. According to this hypothesis, the learner improves and progresses along the 'natural order' when he/she receives second language 'input' that is one step beyond his/her current stage of linguistic competence. For example, if a learner is at a stage 'i', then acquisition takes place when he/she is exposed to 'Comprehensible Input' that belongs to level 'i + 1'. Since not all of the learners can be at the same level of linguistic competence at the same time, Krashen suggests that natural communicative input is the key to designing a syllabus, ensuring in this way that each learner will receive some 'i + 1' input that is appropriate for his/her current stage of linguistic competence.

Finally, the fifth hypothesis, the Affective Filter hypothesis, embodies Krashen's view that a number of 'affective variables' play a facilitative, but non-causal, role in second language acquisition. These variables include: motivation, self-confidence and anxiety. Krashen claims that learners with high motivation, self-confidence, a good self-image, and a low level of anxiety are better equipped for success in second language acquisition. Low motivation, low self-esteem, and debilitating anxiety can combine to 'raise' the affective filter and form a 'mental block' that prevents comprehensible input from being used for acquisition. In other words, when the filter is 'up' it impedes language acquisition. On the other hand, positive affect is necessary, but not sufficient on its own, for acquisition to take place.

O CONSTRUTIVISMO NO ENSINO DE LÍNGUAS

A teoria de Krashen fornece substrato ao Natural Approach e ao Communicative Approach, versões norte-americana e britânica, respectivamente, do construtivismo no ensino de línguas.

O construtivismo preconiza o desenvolvimento de habilidades e conhecimento como resultado de ação, de interação do ser inteligente com seu ambiente. Portanto, o ambiente é fator determinante. No caso de línguas estrangeiras, o ambiente apropriado é aquele que oferece convívio multicultural.

AMBIENTES MULTICULTURAIS DE CONVÍVIO: Ambiente de convívio multiculural ou bicultural é aquele composto de pessoas de diferentes nacionalidades e culturas, que proporciona o desenvolvimento do conhecimento necessário e das habilidades básicas necessárias para que todos possam se comunicar em qualquer situação e nele se sintam à vontade. Quanto maior o grau de afinidade entre seus integrantes, mais completa será a assimilação.

快樂是否能超越分數的限制

快樂是否能超越分數的限制

從認知基模發展談幼整合政策

從認知基模發展談幼整合政策 http://old.npf.org.tw/PUBLICATION/EC/093/EC-R-093-005.htm


教育文化組高級助理研究員 徐明珠


摘要

從認知心理學角度來看，幼兒學習係遵循一套發展的基模，在幼兒不同階段中，經由內在的成熟與外在環境的同化與適應，並結合新舊經驗處理訊息，於過程中主動發現學習，進而建構自我的認知基模。而此一認知基模經由螺旋性的修正，不斷改變賴以為解釋訊息依據的基模，隨著日精月熟，認知基模將越來越精緻化，是以孩童及早啟發，有助於認知發展的奠基，亦即激發基模之可塑性。

教育應堅持專業的崇高理想，在幼托整合方案中，明顯向托育照顧傾斜，忽視幼兒的認知發展，以及學前教育的精神。教育部門不只爭取不到教育向下扎根的權力，就連四歲教育也棄守、五歲亦遭蠶食鯨吞，使得下一代的教育充滿危機。「投資小小孩，國家才有大未來」，本研究嘗試從認知基模的觀點著眼，企圖為複雜的幼托整合問題，歸理出一條明確的方向來。

關鍵字：基模、認知心理學、幼托整合

近日來政府幼托整合政策引起幼教界極大之爭議，原因就是未能考量孩子發展的需求，究竟幼托整合政策應如何訂定，本研究嘗試從心理學家所提出的「認知基模」觀點著眼，企圖為複雜的幼托整合問題，歸理出一條明確的方向來。

壹、 幼兒認知基模

基模（schemes）是人類思考問題的一種基礎模式，由兒童心理學家皮亞傑（Piaget）率先提出。認為嬰幼兒面對外在環境，即有其本能的反應，而這一套處理訊息的認知過程，則稱為基模。基模會隨著年齡及所經歷的學習歷程，不斷的調適或同化產生不同的基模，是以基模不只有量變的增加，也有質變的精進，基模成為個人思考訊息以及學習知識最重要的基礎。

一、 基模的形成

皮亞傑建構了一個具有高度影響力的兒童發展及學習模式，其理論依據發展中的兒童建立認知結構（cognitive structure），亦即心智地圖（mental maps）、基模以及網路化概念的瞭解，和回應所處環境的心理經驗。並進而強調，孩子的認知結構隨著發展逐漸成熟，從最初的哭、吸吮到複雜的心智活動。教育者必須規劃適當的課程，以增強學生的邏輯與概念性成長。而教師必須強調經驗、與週遭環境互動在學生學習上扮演的角色，例如老師必須考慮事物在認知結構上的顯著性。

布魯納（Bruner）認為學習是學習者依照現有及過去知識為基礎而建構概念或新知的主動過程，學習者係藉由認知結構，如基模或心智模式而選擇、改變知識，及建構其假設、形成決策，以對經驗提供意義和組織，並允許個人超越知識所給予的。布魯納提到教學理論包括四個方面：1、具有先備知識，2、瞭解結構化知識的方法，以利於隨時掌握訊息，3、最有效的呈現內容，4、運用報償及處罰的特質和時機。（Bruner 1966）

　

訊息處理論則從訊息處理過程，包括感覺收錄、短期記憶、長期記憶等主要過程，理解知識形成的基模。感覺收錄是對訊息的一種短暫停留，而短期記憶有其同時處理訊息元素容量的侷限性，Sweller建立了跟隨組合個人認知結構基礎，而處理基模以及組合元素的一個理論。長期記憶的內容是允許我們理解、思考和解決問題，而非一群機械式學習事實的精細化結構，這些結構即被稱為是基模，其為組合知識基礎的認知結構。專家與生手之別在於生手無法獲得專家的基礎，學習需要在長期記憶的基礎結構上有所改變，而因為學習者對內容越來越熟悉，與內容有關的認知特色就會調整，而使得其能藉由運作記憶而處理得更有效率。（Sweller, 1988）

至於與皮亞傑出生於同一時期的維果茨基（Vygotsky），則從社會文化的角度出發，不從幼兒本身發展的自然法則出發，而強調社會文化對孩子基模的形成，認為教育對其產生重要的鷹架作用。其主要架構是在社會互動的認知結構上扮演重要的角色，在兒童文化的發展上會出現兩次，一是社會層次on the social level，一是個人層次on the individual level，前者是人際間between people（人際心理學），後者是個體內inside the child（個體內心理學），此可運用於自願性注意、邏輯記憶、概念的形成，不過所有高功能源於個人間的實際運作。而維果茨基也認為認知發展的潛能視兒童從事社會化所得潛能發展區"zone of proximal development" (ZPD)的水平而定，潛能發展區要能全部開發，需與社會充分互動，獲得成人的指導及同儕的協助，以達成個體自我努力無法超越的鷹架作用（Vygotsky 1978）。

從認知心理學角度來看，幼兒學習係遵循一套發展的基模，在幼兒不同階段中，經由內在的成熟與外在環境的同化與適應，並結合新舊經驗處理訊息，於過程中主動發現學習，進而建構自我的認知基模。而此一認知基模經由螺旋性的修正，不斷改變賴以為解釋訊息依據的基模，隨著日精月熟，認知基模越來越精緻化，是以孩童及早啟發，有助於認知發展的奠基，亦即發展基模之可塑性，開發幼兒的無限潛能。

二、基模與學習

基模隱含一種學習歷程的改變，其變遷是由量變到質變、從具體到抽象、從內在到外在。皮亞傑與布魯納將兒童的認知發展分成幾個階段，其不同階段逐有質化的量變，同時從具體思維到產生抽象思維；而比較皮亞傑與維果茨基，則由本能的自我調適發展到外在環境產生的鷹架作用，顯示基模越來越朝成熟化、社會化方向演進。

皮亞傑依據兒童發展階段，提出認知結構四種形式，1、是感覺動作期（sensorimotor stage），零到兩歲，智慧採取感覺動作形式，2、是前運思期（preoperation period），三到七歲，直覺的本能3、具體運思期（concrete operational stage），八到十一歲，能以具體經驗解決問題，4、形式運思期（formal operations stage），十二到十五歲，能做抽象思維。而認知結構的改變經由調適（assimilation）與同化（accommodation）過程，調適是指以現行認知結構解釋事件，然而同化是因應環境需要而改變認知結構，認知的發展是由持久努力於調適與同化所組成。

而布魯納則將認知發展分成動作表徵、形像表徵以及符號表徵三個時期，動作表徵（enactive representation）：係指三歲以下的幼兒靠動作來認識了解周圍的世界，亦即靠感覺動作來獲得知識，如皮亞傑的感覺動作期。形像表徵（iconic representation）：指兒童經由對物體知覺留在記憶中的心像（mental image），或靠照片圖形等，即可獲得知識，如具體運思期。而符號表徵（symbolic representation）：是指運用符號、語言文字為依據的求知方式，如形式運思期。

至於奧蘇貝爾，則提出意義學習論（meaningful learning），認為有意義的學習只能產生於在學生已有充分的先備知識基礎上教他們學習新的知識。換言之，只有配合學生能力與經驗的教學，學生們才會產生有意義的學習。因此基模成為個人認知與學習的概念性結構，居於結構上層者，代表個人對事物的整體認識；而居於下層結構者，代表個人對事物特徵的細部記憶。

D. Rumelhart & D. Norman (1978)提出學習的三個模式：增加（accretion）、結構（structuring）及調節（tuning）。增加是將新的知識附加到現行記憶中，結構是新概念及基模的形成，調節是經由練習對特定工作知識的調整。增加是最常使用的模式，結構性不常出現，需要大費周章，調節是最緩慢的學習形式，也可解釋鐵杵磨成針的原因。 重塑結構是某種反思及頓悟的形式，例如後設認知，且相當於學習的高原現象。另一方面，調節反映的是無法得到反思的自動化行為，例如學習程序。1981年Rumelhart & Norman延伸其模式至類推過程，藉由修正現行基模，並以經驗為本位的方式修正，新的基模將可建立（D. Rumelhart & D. Norman 1978、1981）。

C. Reigeluth根據其精緻理論，教學應以最佳學習的漸增複雜化次序加以組織。例如當從事一件程序性工作的教學時，最先呈現的是工作的第一步，其次是第二個步驟，直到所有步驟完整呈現。精緻化理論主要的關鍵是學習者必須針對隨後的意見及可以被同化的技能發展出一個有意義的脈絡。其有七個主要的策略元素：1、一個精緻化的次序，2、學習必備的次序，3、摘要，4、分析，5、類推，6、認知策略，7、學習者控制。其中以第一項精緻化的次序最重要，將複雜的過程予以簡單化，並把後面的意見及技能化成大要。雖然兩個或多個形式可以同時精緻化，且在應用層次應有幾個基本性或代表性的意見或技能，但呈現大要時仍應以單一的內容形式作為基礎（概念、程序、原則）（C. Reigeluth 1983）。

精緻化的途徑有助於更穩定認知結構的形成，也因此經由有意義脈絡的創造，和允許被動學習者控制內容資訊的準備，就有更好的保存、轉移及動機的增強。精緻理論是奧蘇貝爾前導組織以及布魯納螺旋課程的延伸。生手與專家的問題，Chi et al.認為主要在於指導其認知與問題解決上所擁有的基模，能具備的前導知識越多，所擁有的處理訊息能力越多，知識的精緻化就越有可能（ Chi et al., 1988）。而Quinn & Holland則提到，基模是不同文化認知差異的重要因素，強調文化的變遷因素，教育者能提供所擁有的先備知識基模越廣，越有助於開發兒童的潛能發展區（Quinn & Holland, 1987）。

三、幼兒教育認知基模之建構

認知心理學強調心靈的內在歷程，著重在個體主動的學習驅力，與行為學派重視外在刺激所引發的行為，將學習者視為刺激與反應之間的聯結不同，是故個體經由認識、辨別、理解事物，從而獲得新知識，而經由一歷程，促成個體認知結構的改變或基模的建立，就是一種學習。歸納各認知心理學家理論，所建構出的幼兒教育認知基模如下：

皮亞傑基模應用：認識幼兒心智發展之基模

維果茨基基模之應用：運用可能發展區，開發學生潛能

布魯納基模之應用：從發現學習理論，主動建構知識

奧蘇貝爾基模之應用：從有意義學習論連結新舊知識，產生有意義的學習

是以幼兒教育理論之建構：

1. 從教師到學生為中心：從教師講解說明到培養學生本位

2. 從被動操作到主動建構：從被動學習操作到能主動建構學習

3. 從客觀真理到主觀經驗：從追求客觀的事實到主觀追求知識的創新

4. 從機械記憶到理解發現：從機械式的收錄學習到內化成為理解性的發現學習

5. 從知識傳輸到知識建構：從知識傳輸學習到自我建構知識的學習歷程

貳、 幼兒教育的發展

一、 神經科學：腦本位的學習

孩提時期是腦部發展最神奇的階段，從比例來看，頭部與身體的比例，在胎兒時期為二分之一，出生時為四分之一，成年時降為八分之一。再從腦的重量來看，出生約為四百公克，為成人腦的四分之一；六個月時增加為兩倍，一歲時增加到三倍，是成人腦的七十五％；三歲時即達成人腦的八十％，速度十分驚人，因此腦部應即早開發，對孩子認知發展越有助益。

神經科學主要研究人類的神經系統、腦以及知覺、認知、記憶和學習的生理基礎。神經系統與腦是人類學習過程的基礎，其連結我們對認知行為過程的觀察及支持此種行為的生理行為。主要的發現是，腦包含三項結構，一是低等腦、爬蟲類腦（lower or reptilian brain） 控制基礎知覺的功能；二哺乳動物腦、邊緣葉腦（mammalian or limbic brain），控制感情記憶；三 是新皮質腦或思考腦（neocortex or thinking brain），控制認知、理性、語言和高智能。人腦並非電腦，其神經結構的連結是鬆散、彈性、網狀以及重疊的，像電腦線性及平行的處理過程是可能的，然而腦卻被形容是自我組織系統。人類的神經細胞是由樹狀突分枝所聯結，大約有一萬億個神經元，和一千兆個連接點，聯結起來可以產生百萬至千萬的能量。運用腦加強連結形式，越有助於下次的連結，而這就是記憶發展的原因。當教育者考慮神經科學時，其設計課程環繞在真實的經驗和整合人的全意志，並將教學集中在增進複雜思考及腦的開發上，重視本位的學習，因此神經科學者主張繼續教育以及成人的智能發展。

腦本位的學習（Brain-based learning）是建立在腦的結構與功能上，只要腦能正常運作，將會產生學習。腦本位學習的核心原則，腦為一平行處理者，可同時執行幾項活動，例如聞或嚐；學習涉及生理學；找尋內在的意義；找尋來源於形式的意義；情感對於形式很重要；同時進行全部及部分的過程；學習包含注意與周邊的認知；學習包含知覺與非知覺的過程；我們有空間與機械兩種記憶的形式；事實是依自然和空間記憶而來；學習由挑戰和威脅而強化；每個腦都是獨一的。與教學有關的三點：一是弦樂式是浸潤（Orchestrated immersion），創造學習的環境；二是放鬆式的警覺（Relaxed altertness），減少學習者的恐懼；三是主動處理式（Active processing），經由主動過程凝聚內化資訊。尤其教學者必須重視各種學習形式，以符合個體認知發展的需求。

而學習形式（learning style）的概念是根植於心理學形式的分類，其理論是依據研究顯示，例如遺傳的結果、幼年時期的養育、和當前環境的需求，不同的個人認知與處理資訊的傾向也不同。其作法可以分成：一是具體及抽象的認知者（Concrete and abstract perceivers）：具體認知者經由做、行為以及感覺等直接經驗吸收資訊。但是抽象的認知者經由分析、觀察以及思考獲得資訊。二是主動而反應過程者（Active and reflective processors）：主動處理者使用新資訊使得經驗有意義。反應過程者藉由反應與思考使經驗有意義。傳統學制傾向於贊成吸收認知和反應過程。而其他的學習方式並不能從課程、教學與評鑑上獲得回報與反應。除了傳統分析技能、理由以及連續性的解決問題能力，教育者必須強調直覺、感覺、敏感性與想像力。

在教學方面，老師應設計其教學方法連結四種學習形式，綜合使用各種經驗、反應、概念化以及實驗等。教學者可以引進寬廣不同的企業元素進入教室，例如聲音、音樂、視覺的、動作、經驗以及談話等。至於評鑑方式，老師應採多元的評鑑技巧，聚焦於全腦開發能量以及每一種不同的學習形式。

二、 開發多元智慧：建構式學習

近年來揚棄傳統由教師導向的學習，轉而重視主動建構式的學習。建構主義的幾項指導原則：一、學習是意義的尋找，因此學習必須由學生主動建構意義而發生。二、有意義的學習必須理解全部或部分，而部分必須放在整體脈絡中去理解。因此學習過程必須集中在主要的概念上，而非孤立的事實。三、為了產生教學成效，必須了解學生認知世界的模式及其支持這些模式的假設。四、學習的目的是為了個人建構自己的學習，而不只是記住對的答案或回溯其他的意義。其對課程之衝擊，建構主義需要排除標準化課桯，相反的，要針對學生的先備知識打造學生適性課程，也就是強調解決問題的能力。教學策略看學生的反應，並鼓勵其分析、解釋及預測資訊，而老師也要用開放式問題，增進學生間廣泛的對話。在評鑑方面，建構主義需要排除標準化測驗，而使得評鑑成為學習過程的一部分，促使學生在自我知識建構過程中扮演更重要的角色。

建構主義的概念在於開發多元智慧，不將學習定位於只有一種思考、只有一元價值上， Guilford’s智慧結構理論包括運思、內容以及產品三大部分，其中運思部分又包括認知、記憶、分歧產品、聚合產品以及評估五項；內容部分又包括單元、班級、關係、系統、轉換、以及言外之意六部分；而產品部分包含視覺的、聽覺的、符號的、語意的以及行為的五項。既然每一個面向都是獨立的，理論上智慧包含了150個不同的內容，開發幼兒的智慧應通盤考量各面向，建立不同階段不同性向幼兒的認知基模，針對不同性向孩子，在運思、內容及產品三大不同的向度上做適才適性的開發。（Guilford）



Guilford's Structure of Intellect (SI) theory



認知發展有其階段性步驟，綜合各項資料顯示：

1至1.5歲：口頭語言學習的關鍵期；2-3歲：幼兒計數能力開始發展的時期，幼兒開始自我約束、學習掌握各種規矩和要求的關鍵期；3 至5 歲：發展幼兒音樂能力的關鍵期；4至5歲：學習書面語言，掌握用筆能力的關鍵期；3至8歲：學習外語能力的關鍵期（3至5歲是口語，6至八歲可學習書面語言）；5 歲左右：掌握數學概念的關鍵期，學習加減法速度很快；5至6歲：掌握漢語詞彙、理解詞彙意義的關鍵期；6至16歲：體育能力最有效的關鍵期；6至7 歲：速度、靈活性發展的關鍵期；11至16 歲：力量和耐力增長的關鍵期。

三、 及早教育成趨勢

不論從認知發展及神經科學來看，越早啟蒙對孩子越有助益。學前教育於二十世紀下旬快速增加，最大一波是1960年代美國訂定的提早入學計畫，協助那些經濟弱勢家庭克服認知、社會及情感和生理的不足，藉由提供教育及社會的活動，使得這些孩子可以獲得學校的經驗，並有中產階級提出提早入學的呼聲，許多的教育者及研究者也認為早期教育對孩子的認知及社會發展有助益。但仍有持不同的觀點者，Elkind 、Katz 、Zigler反對過早接觸正式及高度結構化的教育（Elkind 1988、Katz1987、Zigler1986），反對理由為，因為其接受的是學齡兒童的課程，並不適宜六歲以下孩子接受，幼兒教育應建立高品質行政人員及課程，提供綜合性、健康、社會服務及家長參與的課程。除此之外，Herman、Puleo則對半日制或全日制感興趣，其認為額外的時數會讓幼兒疲乏，增加時間不會強化課程的品質（Herman1984、Pule1988）。

有關的幼兒研究，認為幼兒教育不同的模式，如說教式（didactic）、老師指導式（teacher-directed）、較少結構性（less- structured）、以及發現模式等，視不同性向的孩子採用不同的模式。在課程內容方面，Huston-Stein發現，多些孩子選擇的較少結構性課程活動比激勵想像力、工作和獨立有效（Huston-Stein 1977）。

在班級大小方面，生師比不應超過16：1，許多人同意四歲小幼兒是10：1，1985年芝加哥公立學校發現幼兒在半天小班級的16：1比全天的28：1效果較好。Robinson、Wittebols提到，小班級能引起個人更多注意，且處理大班級不能處理之事（Robinson、Wittebols,1986）。

全日制原為提供弱勢孩子增加學習的機會，以改進其成功的機會，然而全日、半日也有一番討論。不少研究發現，弱勢學生從全日制收到短期的效果甚於傳統半日制的幼稚園。Nieman and castright ，發現弱勢學生進入學前教育、全日制幼稚園比未能進入學前教育及只進入半日制幼稚園表現得好。（Nieman and castright 1981）

學前教育對孩子有其功用，尤其可以保障弱勢學生受教權。在課程內容上應與小學教育有所切割，必須提供多元性啟發，避免正式結構化的課程，且最好是小班制，全日制或半日制看孩子情況而定。幼兒教育是學習扎根的黃金時段，是以幼兒不應只有保育，而忽略了智力的及早開發、學習，其主要概念如下：

1、針對幼兒的發展，提供合適的教學資源及教學活動。

2、對孩子擁有高度的期待，採取步驟且確保能為孩子下階段的成功做準備。

3、確認這些活動由先前到未來的活動及學習，並解釋是對孩子部分活動的這些連結。

4、先檢視課程，給予明確指導，考核學生學習情況

5、溫情交流、吁寒問暖

6、各階段間要有一致性，確認老師懂得其課程適合學校的整體課程。

7、分配與運用時間，迎合課程目標

8、以強調技能學習的方式提供行政人員發展的機會

9、提供家長參與機會，培養老師與家長協同教學的能力

10、藉以透過良好的設計，使弱勢幼兒過更好的生活。

參、 幼托整合的問題

隨著雙薪家庭和及早學習觀念成形，家長對於幼托機構的需求越來越殷切，隨之衍生幼兒保育與教育的問題。由於幼兒階段是認知發展變化最快的時期，加以及早教育的觀念逐漸成形，因此在教育與保育年齡的切割以及管轄機構的歸屬上長期以來存在極大的爭議，造成幼兒教保品質無法統整的困難。近年來，國內社政機構與教育機構已有幼托整合的呼聲，然因兩者之間存在著即大的落差，因此整合不易。其問題如下：

一、功能屬性：幼稚園為教育體系，托兒所為福利機構，兩者之間整合，涉及功能屬性不同的問題。

二、主管機關：幼稚園與托兒所，分屬教育及社政單位管理，管理機制不同。但87年精省後，托兒所設置標準及設立辦法業已授權各地方政府權責辦理，與幼稚園主要法令仍由中央教育行政機關主管不同。

三、法令依據不同：

幼稚園：相關立案及規範依據幼稚教育法暨施行細則、幼稚園設備標準、幼稚園課程標準辦理。師資則依教師法、師資培育法、高級中等以下學校及幼稚園教師資格檢定、教師實習法規定辦理。

托兒所：依據兒童福利法、托兒所立案須知、兒童福利機構設置標準與設立條例、兒童福利專業人員資格要點、兒童福利專業人員訓練實施方案、台灣省各縣市托兒所組織準則。

四、年齡層重疊：四至六歲重疊，越接近小學入學階段，入園所比例越高。

五、師資資格及進用：

幼稚園須具備師資培育法取得教師資格，進用依據幼稚教育法及師資培育法相關規定聘任教師。

托兒所：依兒童福利專業人員資格要點取得保育人員資格，並依據要點規定辦理進用人員。公立托兒所人員並應具備公務人員資格。

但據教育部九十年全國幼教普查發現，四成一不合格教師（私立五成四）須有後續輔導措施。

六、立案及相關設備：

兩者因立案條件有不同，而有實際差異，立案條件重整後，是否仍能合於規定，過度期間要如何因應。

七、課程與教學：

托兒所雖有教保手冊，但幾乎多以幼稚園教學設施、教材及課程設計，從事幼教活動，並無太大的差別。但因應國教向下延伸，教學應有重新的定位及調整。

儘管幼托整合存有極大困難，但是不少幼教界人士仍然贊成幼托應予以整合的主要原因有二：

一、幼兒托育與教育整合是台灣當前幼兒照顧與教育制度改革的重要議題之一。主要目的在處理現行「幼稚教育」與「托育服務」體制中，六歲以下兒童教育與托育功能重疊所衍生的問題。

二、希望師資合流共用，幼稚園須含保育服務，而對於4至6歲托兒機構也給予教育的功能，相互為用。

在目前幼稚園托兒所化、托兒所幼稚園化的發展現況下，兩個主管機關、兩種專業人員資格標準與兩套設施辦法，致使「相同年齡的孩子，在兩類不同機構中，接受兩種不同品質的照顧與教育」，因此幼稚園與托兒所理論上雖然扮演不同社會功能，但隨著社會的變遷，幼托整合可能是時代的需要與必然的趨勢。

肆、 幼托整合政策的爭議

日前教育部與內政部共同宣布「幼托整合方案」草案，並預定於九十五學年全面實施國教向下延伸，不過這些幼教政策並未能獲得幼教界的支持，主要原因就是缺乏孩童本位考量，忽略認知基模的發展需求。

一、 幼托整合方案向托育傾斜：

國內幼教雙軌制度行之有年，在兩個主管機關、兩種專業人員資格標準與兩套法規的割裂下，致使相同年齡的孩子，在兩類不同的機構中，接受兩種不同品質的照顧與教育。為了解決幼托重疊所衍生的問題，整合幼教師資，以及確保幼兒教育的均質化等，幼托整合的呼聲早已有之。此次政府規劃的幼托整合方案，係將零至二足歲幼兒，劃歸家庭托育與托嬰中心；幼稚園和托兒所整合為「幼兒園」，辦理二至五歲幼兒幼托工作，兩者主管機關均為內政部；五足歲幼兒劃歸為「國民教育幼兒班」，主管機關為教育部，幼托政策明顯向托育照顧傾斜，忽視學前教育的精神，並不符合多數幼教界人士的期待。

二、 幼托整合未能解決幼教問題：

幼托整合的宗旨是整合立案標準、師資水準及教保品質，但草案僅著眼於形式的統一，卻無視於品質的齊一，將五歲以下的幼兒全數歸為內政部主管，不僅未能照顧到多數幼兒的教育，同時也把四歲幼教品質降至托育層次。目前幼托教師係由不同師資標準所規範，整合應是提升托兒所保育員至教師層次，供給幼教老師進修機會，使其得以依法取得合格教師資格，確保幼兒的受教權；但規劃中的幼托整合方案，係將2至5 歲從業人員稱為教保員或助理教保員，5至6歲從業人員稱教師，與現行幼托教師分流的情況並無不同，凸顯幼托人員資格的階級化。雖然政府鼓勵幼兒園的保育人員進修，取得合格教師的資格，表面上看起來是一種激勵措施，但可能造成幼教師流向國幼班，將托育工作推向品質沈淪的深淵。

三、幼托整合方案經費龐大、以多做少：

規劃中的幼托整合方案對國幼班的界定仍為幼稚園，並非「六加一」的國民小學教育，亦不符「義務性、強迫性、免費性」國民教育的精神。公幼每年補助五千元，私立兩萬元，估計每年增加經費需要四、五十億元，希望提高幼兒的入園率，但事實上幼教問題不患寡而患不均。根據教育部調查，幼兒入園率已達九成六以上，僅偏遠地區入園率較低，應實踐重點性的公平正義，而不應以多做少，無視於資源分配的效率化。

四、幼托整合方案與多元智慧不符：

為了爭取幼教經營者的支持，整合方案中規劃，幼兒園除了可向下收托0-5歲學齡前幼兒外，亦可往上提供6-12歲學齡兒童之課後照顧，看起來幼托機構服務對象似乎擴大了，但卻忽略學齡前幼兒及學齡後兒童教育的需求。皮亞傑將嬰幼兒至青少年分成感覺動作期（出生至2 歲）、前運思期（2至7歲）、具體運思期（7至11歲）及形式運思期（11至15 歲），依據不同認知發展進程決定學習內容。政府將其通通擺盡托育機構，棄孩童的教育於不顧，與教改所要倡導的「多元智慧」理念不符。

五、幼托整合方案獨尊托育棄守教育：

隨著工業化及都市化社會的結構變遷與急遽發展，成長中兒童的照顧成為家長迫切的福利需求，而期待政府給予家庭外服務體系的支持，然而卻不宜獨尊托育而棄守教育。學習是孩子的本能，也是一種權利。不論從神經生理學或認知心理學的角度來看，都說明越早啟蒙所能聯結的神經細胞及學習網路，就越綿密廣布、能處理的訊息也越多。在政府放棄對幼兒教育的責任後，其腦力將在「只有照顧沒有品質、只有托育缺乏教育」中受到箝制。

六、幼托整合方案使幼教從量變到質變：

原本國教向下延伸是政府在規劃延長國教時的一項方案，如今既已宣示「國幼班」仍為幼稚教育階段，已無辜成為終結四歲教育的政策祭品，甚至五歲教育也難逃質變的命運。托育制度將大量進駐公立幼稚園，致使公幼學生被切割為「上午教育、下午保育」，逼迫其放棄均質化的全日制教育，接受二度就業婦女的非專業性托育。擴大婦女就業有許多途徑，教育為一專業性工作，由二度就業婦女投入，既缺乏專業性、也無正當性，更因此而犧牲孩童們的教育。

伍、 幼托整合政策的建議

幼托整合涉及結構面與實務面通盤考量之問題，問題極其複雜，幼稚園及托兒所既以幼兒為服務對象，需符應幼兒身心發展需求，教育保育內涵尤應兼顧。過去我國長期以教育與托育雙軌制度實施，各就專長與權責分工合作，如今在家庭、社會結構與趨勢改變的前提下，為能有效提升幼托機構服務品質，應以溫和漸進方式，階段性逐步規劃整合方向，周全配套，將幼兒教育納入教育體系管理，實施幼托一元化。

一、 重視及早教育的趨勢：從認知基模來看，幼兒學習係遵循一套發展的基模。在幼兒不同階段中，經由內在的成熟與外在環境的同化與適應，並結合新舊經驗處理訊息，於過程中主動發現學習，進而建構自我的認知基模。是以幼兒的發展係從量變到質變、從具體到抽象，透過教育的鷹架作用，可以促成幼兒潛能的及早開發。幼托整合應重視孩子的認知，考量幼兒發展的需要。

二、實施幼托一元化：目前幼托整合方案，將五歲以下全部歸為社政單位，以照顧及保育取代學習，忽略了五歲以下孩子教育的需求。幼兒出生零至二歲，最需重視的是衛生保健、身心安全，而如果有先天或後天的身心疾病，也正是及早診斷、及早治療的重要時機，此一階段的照顧、保健工作，適合由衛生保健機關負責。至於二歲之後，幼托必須全面一元化，由教育部單一機關負責，提供促進幼兒發展最有利的整合服務。

三、國教向下延伸定位：目前五歲的國民教育幼兒班，既已定位於教育，但應與現在五歲教育有所不同，教育的內容應依照幼兒身心發展的歷程規劃，避免成為小一的先修班或促使升學競爭的馬拉松賽提早開跑。同時要銜接九年一貫，融入領域統整及幼兒本位概念，有關的課程及師資須加以因應，訂定國教向下延伸一貫學習內容，避免重蹈九年一貫課程倉卒實施、配套不周的覆轍。

四、建立幼教經費保障制度：規劃中的幼托整合方案經費龐大，遠高於發放幼兒教育券，只為提高入學率，政府應以少做多，重視學齡前的教育。為了保障幼教經費，實施專款專用，建議依照特殊教育法、原住民族教育法規範幼稚園及托兒所的經費，在中央、地方政府皆占一定比例，以維幼稚園及托兒所教育品質。

五、齊一公私及幼托退撫：幼托整合應齊一公私立，以及幼托老師退撫標準，才能吸引好的幼教師留任，專注幼兒教育的發展。同時要以證換證、以照換照，維護現有教師、保育員工作權，現已在職幼稚園教師、保育員，以現有證照轉換合格證書，未符合登記資格者，由政府提供在職進修機會，使其取得合格證書。而現已合法登記的幼稚園及托兒所，也以立案執照換取新的執照。

六、幼教政策完整配套：重視教育扎根，幼托納入一元體系管理，包括公私立幼稚園均質化、在偏遠地區增設幼稚園、不合格幼稚園教師進修、對就讀私立幼稚園學童的補助、幼稚園與國小課程的銜接、師資培育、立案基準以及法令修改等問題皆應有縝密的規劃。

不論從認知心理學或神經生理學角度來看，都說明越早啟蒙所能聯結的神經細胞及學習網路，就越綿密廣布、能處理的訊息也越多。從認知心理學角度來看，幼兒學習係遵循一套發展的基模，在幼兒不同階段中，經由內在的成熟與外在環境的同化與適應，並結合新舊經驗處理訊息，於過程中主動發現學習，進而建構自我的認知基模。而此一認知基模經由螺旋性的修正，不斷改變賴以為解釋訊息依據的基模，隨著日精月熟，認知基模越來越精緻化，是以孩童及早啟發，有助於認知發展的奠基，亦即發展基模之可塑性。

教育應堅持專業的崇高理想，在幼托整合方案中，係將零至二足歲幼兒，劃歸家庭托育與托嬰中心；幼稚園和托兒所整合為「幼兒園」，辦理二至五歲幼兒幼托工作，兩者主管機關均為內政部；五足歲幼兒劃歸為「國民教育幼兒班」，主管機關為教育部，幼托政策明顯向托育照顧傾斜，忽視學前教育的精神。教育部門不只爭取不到教育向下扎根的權力，就連四歲教育也棄守、五歲亦遭蠶食鯨吞，使得下一代的教育充滿危機。幼托整合方案應三思而後行，需滿足幼兒認知發展的需求，兼顧現代社會與家庭托育的服務，並齊一幼托教師水準，同時整合幼托資源，提升幼兒教育品質，絕不能讓決策的無能，貽誤孩子受教的黃金時機。有關國教向下延伸、零至六歲幼托業務工分、內容規劃及銜接國民教育的問題都應有先期的規劃，使政策得以順利的推動，投資我們的小小孩，國家才有大未來。（93.07.國家政策論壇季刊夏季號）



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Overview: Experimental and Quasi-Experimental Research

To read about experimental and quasi-experimental research, click on the items below:

Introduction
Basic Concepts of Experimental and Quasi-experimental Research
Methods: Five Major Steps
Issues and Commentary
Key Terms
Related Links
Annotated Bibliography
Contributors to this Guide

Experimental and Quasi-Experimental Research: Introduction
You approach a stainless-steel wall, separated vertically along its middle where two halves meet. After looking to the left, you see two buttons on the wall to the right. You press the top button and it lights up. A soft tone sounds and the two halves of the wall slide apart to reveal a small room. You step into the room. Looking to the left, then to the right, you see a panel of more buttons. You know that you seek a room marked with the numbers 1-0-1-2, so you press the button marked "10." The halves slide shut and enclose you within the cubicle, which jolts upward. Soon, the soft tone sounds again. The door opens again. On the far wall, a sign silently proclaims, "10th floor."

You have engaged in a series of experiments. A ride in an elevator may not seem like an experiment, but it, and each step taken towards its ultimate outcome, are common examples of a search for a causal relationship-which is what experimentation is all about.

You started with the hypothesis that this is in fact an elevator. You proved that you were correct. You then hypothesized that the button to summon the elevator was on the left, which was incorrect, so then you hypothesized it was on the right, and you were correct. You hypothesized that pressing the button marked with the up arrow would not only bring an elevator to you, but that it would be an elevator heading in the up direction. You were right.

As this guides explains, the deliberate process of testing hypotheses and reaching conclusions is an extension of commonplace testing of cause and effect relationships.


Basic Concepts of Experimental and Quasi-Experimental Research
Discovering causal relationships is the key to experimental research. In abstract terms, this means the relationship between a certain action, X, which alone creates the effect Y. For example, turning the volume knob on your stereo clockwise causes the sound to get louder. In addition, you could observe that turning the knob clockwise alone, and nothing else, caused the sound level to increase. You could further conclude that a causal relationship exists between turning the knob clockwise and an increase in volume; not simply because one caused the other, but because you are certain that nothing else caused the effect.

To read about basic concepts of experimental and quasi-experimental research, click on the items below:

Independent and Dependent Variables
Treatment and Hypothesis
Causality
Matching and Randomization
Differences between Quasi-Experimental and Experimental Research


Independent and Dependent Variables
Beyond discovering causal relationships, experimental research further seeks out how much cause will produce how much effect; in technical terms, how the independent variable will affect the dependent variable. You know that turning the knob clockwise will produce a louder noise, but by varying how much you turn it, you see how much sound is produced. On the other hand, you might find that although you turn the knob a great deal, sound doesn't increase dramatically. Or, you might find that turning the knob just a little adds more sound than expected. The amount that you turned the knob is the independent variable, the variable that the researcher controls, and the amount of sound that resulted from turning it is the dependent variable, the change that is caused by the independent variable.

Experimental research also looks into the effects of removing something. For example, if you remove a loud noise from the room, will the person next to you be able to hear you? Or how much noise needs to be removed before that person can hear you?


Treatment and Hypothesis
The term treatment refers to either removing or adding a stimulus in order to measure an effect (such as turning the knob a little or a lot, or reducing the noise level a little or a lot). Experimental researchers want to know how varying levels of treatment will effect what they are studying. As such, researchers often have an idea, or hypothesis, about what effect will occur when they cause something. Few experiments are performed where there is no idea of what will happen. From past experiences in life or from the knowledge we possess in our specific field of study, we know how some actions cause other reactions. Experiments confirm or reconfirm this fact.


Causality
Experimentation becomes more complex when the causal relationships they seek aren't as clear as in the stereo knob-turning examples. Questions like "Will olestra cause cancer?" or "Will this new fertilizer help this plant grow better?" present more to consider. For example, any number of things could affect the growth rate of a plant-the temperature, how much water or sun it receives, or how much carbon dioxide is in the air. These variables can affect an experiment's results. An experimenter who wants to show that adding a certain fertilizer will help a plant grow better must ensure that it is the fertilizer, and nothing else, affecting the growth patterns of the plant. To do this, as many of these variables as possible must be controlled.

View an Example


Matching and Randomization
Up to this point, we have been discussing an example in terms of one MegaGro plant, one Plant! plant, and one control plant. But even though you have tried to remove all extraneous variables, results may appear merely coincidental. Since you want to prove a causal relationship in which a single variable is responsible for the effect produced, the experiment would produce stronger proof if the results were replicated in larger treatment and control groups.

Selecting groups entails assigning subjects in the groups of an experiment in such a way that treatment and control groups are comparable in all respects except the application of the treatment. Groups can be created in two ways: matching and randomization. In the MegaGro experiment, the plants might be matched according to characteristics such as age, weight and whether they are blooming. This involves distributing these plants so that each plant in one group exactly matches characteristics of plants in the other groups. Matching may be problematic, though, because it "can promote a false sense of security by leading [the experimenter] to believe that [the] experimental and control groups were really equated at the outset, when in fact they were not equated on a host of variables" (Jones, 291). In other words, you may have flowers for your MegaGro experiment that you matched and distributed among groups, but other variables are unaccounted for. It would be difficult to have equal groupings.

Randomization, then, is preferred to matching. This method is based on the statistical principle of normal distribution. Theoretically, any arbitrarily selected group of adequate size will reflect normal distribution. Differences between groups will average out and become more comparable. The principle of normal distribution states that in a population most individuals will fall within the middle range of values for a given characteristic, with increasingly fewer toward either extreme (graphically represented as the ubiquitous "bell curve").

NOTE: for more information link to random sampling in the Survey section.



Differences between Experimental and Quasi-Experimental Research
Thus far, we have explained that for experimental research we need:

a hypothesis for a causal relationship;
a control group and a treatment group;
to eliminate confounding variables that might mess up the experiment and prevent displaying the causal relationship; and
to have larger groups with a carefully sorted constituency; preferably randomized, in order to keep accidental differences from fouling things up.
But what if we don't have all of those? Do we still have an experiment? Not a true experiment in the strictest scientific sense of the term, but we can have a quasi-experiment, an attempt to uncover a causal relationship, even though the researcher cannot control all the factors that might affect the outcome.

A quasi-experimenter treats a given situation as an experiment even though it is not wholly by design. The independent variable may not be manipulated by the researcher, treatment and control groups may not be randomized or matched, or there may be no control group. The researcher is limited in what he or she can say conclusively.

The significant element of both experiments and quasi-experiments is the measure of the dependent variable, which it allows for comparison. Some data is quite straightforward, but other measures, such as level of self-confidence in writing ability, increase in creativity or in reading comprehension are inescapably subjective. In such cases, quasi-experimentation often involves a number of strategies to compare subjectivity, such as rating data, testing, surveying, and content analysis.

Rating essentially is developing a rating scale to evaluate data. In testing, experimenters and quasi-experimenters use ANOVA (Analysis of Variance) and ANCOVA (Analysis of Co-Variance) tests to measure differences between control and experimental groups, as well as different correlations between groups. For details about these two common types of tests, refer to the Statistics unit. Survey and content analysis are also detailed elsewhere in this Website.

Since we're mentioning the subject of statistics, note that experimental or quasi-experimental research cannot state beyond a shadow of a doubt that a single cause will always produce any one effect. They can do no more than show a probability that one thing causes another. The probability that a result is the due to random chance is an important measure of statistical analysis and in experimental research.

In the Methods section, we provide more details and a step-by-step scenario, as well as add steps to those so far described.


Methods: Five Steps
Experimental research can be roughly divided into five phases:

Identifying a research problem
Planning an experimental research study
Conducting the experiment
Analyzing the data
Writing the paper/presentation describing the findings
In this section, we explore how an experiment or quasi-experiment is constructed step-by-step, noting that these steps are by no means exhaustive. We sketch them out to provide a rudimentary understanding of how the process works.

To read about methods in experimental and quasi-experimental research, click on the items below:

Identifying a research problem
Planning an experimental research study
Conducting the experiment
Analyzing the data
Reporting the Results

Step One: Identifying a Research Problem
The process starts by clearly identifying the problem you want to study and considering what possible methods will affect a solution. Then you choose the method you want to test, and formulate a hypothesis to predict the outcome of the test.

For example, you may want to improve student essays, but you don't believe that teacher feedback is enough. You hypothesize that some possible methods for writing improvement include peer workshopping, or reading more example essays. Favoring the former, your experiment would try to determine if peer workshopping improves writing in high school seniors. You state your hypothesis: peer workshopping prior to turning in a final draft will improve the quality of the student's essay.


Step Two: Planning
The next step is to devise an experiment to test your hypothesis. In doing so, you must consider several factors. For example, how generalizable do you want your end results to be? Do you want to generalize about the entire population of high school seniors everywhere, or just the particular population of seniors at your specific school? This will determine how simple or complex the experiment will be. The amount of time funding you have will also determine the size of your experiment.

Continuing the example from step one, you may want a small study at one school involving three teachers, each teaching two sections of the same course. The treatment in this experiment is peer workshopping. Each of the three teachers will assign the same essay assignment to both classes; the treatment group will participate in peer workshopping, while the control group will receive only teacher comments on their drafts.


Step Three: Conducting the Experiment
At the start of an experiment, the control and treatment groups must be selected. Whereas the "hard" sciences have the luxury of attempting to create truly equal groups, educators often find themselves forced to conduct their experiments based on self-selected groups, rather than on randomization. (See more about this in the discussion of Advantages and Disadvantages of Experimental Research.) As was highlighted in the Basic Concepts section, this makes the study a quasi-experiment, since the researchers cannot control all of the variables.

For the peer workshopping experiment, let's say that it involves six classes and three teachers with a sample of students randomly selected from all the classes. (See random sampling for more on this.) Each teacher will have a class for a control group and a class for a treatment group. The essay assignment is given and the teachers are briefed not to change any of their teaching methods other than the use of peer workshopping. You may see here that this is an effort to control a possible variable: teaching style variance.


Step Four: Analyze the Data
The fourth step is to collect and analyze the data. This is not solely a step where you collect the papers, read them, and say your methods were a success. You must show how successful. You must devise a scale by which you will evaluate the data you receive, therefore you must decide what indicators will be, and will not be, important.

Continuing our example, the teachers' grades are first recorded, then the essays are evaluated for a change in sentence complexity, syntactical and grammatical errors, and overall length. Any statistical analysis is done at this time if you choose to do any. Notice here that the researcher has made judgments on what signals improved writing. It is not simply a matter of improved teacher grades, but a matter of what the researcher believes constitutes improved use of the language.

For a more in-depth discussion of this step, read the Statistics unit.


Step Five: Report the Results
Once you have completed the experiment, you will want to share findings by publishing academic paper (or presentations). These papers usually have the following format, but it is not necessary to follow it strictly. Sections can be combined or not included, depending on the structure of the experiment, and the journal to which you submit your paper (see more in Rhetoric and the Presentation of Research in English).

Abstract: Summarize the project: its aims, participants, basic methodology, results, and a brief interpretation.
Introduction: Set the context of the experiment.
Review of Literature: Provide a review of the literature in the specific area of study to show what work has been done. Should lead directly to the author's purpose for the study.
Statement of Purpose: Present the problem to be studied.
Participants: Describe in detail participants involved in the study; e.g., how many, etc. Provide as much information as possible.
Materials and Procedures: Clearly describe materials and procedures. Provide enough information so that the experiment can be replicated, but not so much information that it becomes unreadable. Include how participants were chosen, the tasks assigned them, how they were conducted, how data were evaluated, etc.
Results: Present the data in an organized fashion. If it is quantifiable, it is analyzed through statistical means. Avoid interpretation at this time.
Discussion: After presenting the results, interpret what has happened in the experiment. Base the discussion only on the data collected and as objective an interpretation as possible. Hypothesizing is possible here.
Limitations: Discuss factors that affect the results. Here, you can speculate how much generalization, or more likely, transferability, is possible based on results. This section is important for quasi-experimentation, since a quasi-experiment cannot control all of the variables that might affect the outcome of a study. You would discuss what variables you could not control.
Conclusion: Synthesize all of the above sections.
References: Document works cited in the correct format for the field.

Experimental and Quasi-Experimental Research: Issues and Commentary
Several issues are addressed in this section, including the use of experimental and quasi-experimental research in educational settings, the relevance of the methods to English studies, and ethical concerns regarding the methods.

To read about issues in experimental and quasi-experimental research, click on the items below:

Using Experimental and Quasi-Experimental Research in Educational Settings
Relevance to English Studies
Advantages and Disadvantages of Experimental Research: Discussion
Advantages and Disadvantages of Experimental Research: Quick Reference List
Ethical Concerns


Using Experimental and Quasi-Experimental Research in Educational Settings
Click on the items below to read about:

Charting Causal Relationships in Human Settings
Combining Theory, Research, and Practice
Bias and Rigor

Experimental and Quasi-Experimental Research
Back to Experimental and Quasi-Experimental Research in Human Settings

Any time a human population is involved, prediction of casual relationships becomes cloudy and, some say, impossible. Many reasons exist for this; for example,

researchers in classrooms add a disturbing presence, causing students to act abnormally, consciously or unconsciously;
subjects try to please the researcher, just because of an apparent interest in them (known as the Hawthorne Effect); or, perhaps
the teacher as researcher is restricted by bias and time pressures.
But such confounding variables don't stop researchers from trying to identify causal relationships in education. Educators naturally experiment anyway, comparing groups, assessing the attributes of each, and making predictions based on an evaluation of alternatives. They look to research to support their intuitive practices, experimenting whenever they try to decide which instruction method will best encourage student improvement.

Experimental and Quasi-Experimental Research
Back to Experimental and Quasi-Experimental Research in Human Settings

The goal of educational research lies in combining theory, research, and practice. Educational researchers attempt to establish models of teaching practice, learning styles, curriculum development, and countless other educational issues. The aim is to "try to improve our understanding of education and to strive to find ways to have understanding contribute to the improvement of practice," one writer asserts (Floden 1996, p. 197).

In quasi-experimentation, researchers try to develop models by involving teachers as researchers, employing observational research techniques (see the observational research guide). Although results of this kind of research are context-dependent and difficult to generalize, they can act as a starting point for further study. The "educational researcher . . . provides guidelines and interpretive material intended to liberate the teacher's intelligence so that whatever artistry in teaching the teacher can achieve will be employed" (Eisner 1992, p. 8).


Experimental and Quasi-Experimental Research
Back to Experimental and Quasi-Experimental Research in Human Settings

Critics contend that the educational researcher is inherently biased, sample selection is arbitrary, and replication is impossible. The key to combating such criticism has to do with rigor. Rigor is established through close, proper attention to randomizing groups, time spent on a study, and questioning techniques. This allows more effective application of standards of quantitative research to qualitative research.

Often, teachers cannot wait to for piles of experimentation data to be analyzed before using the teaching methods (Lauer and Asher 1988). They ultimately must assess whether the results of a study in a distant classroom are applicable in their own classrooms. And they must continuously test the effectiveness of their methods by using experimental and qualitative research simultaneously. In addition to statistics (quantitative), researchers may perform case studies or observational research (qualitative) in conjunction with, or prior to, experimentation.


Relevance to English Studies
Click on the items below to read about:

Situations in English Studies that Might Encourage use of Experimental Methods
Transferability-Applying Results
Concerns English Scholars Express about Experiments

Experimental and Quasi-Experimental Research
Back to Relevance to English Studies

Whenever a researcher would like to see if a causal relationship exists between groups, experimental and quasi-experimental research can be a viable research tool. Researchers in English Studies might use experimentation when they believe a relationship exists between two variables, and they want to show that these two variables have a significant correlation (or causal relationship).

A benefit of experimentation is the ability to control variables, such as the amount of treatment, when it is given, to whom and so forth. Controlling variables allows researchers to gain insight into the relationships they believe exist. For example, a researcher has an idea that writing under pseudonyms encourages student participation in newsgroups. Researchers can control which students write under pseudonyms and which do not, then measure the outcomes. Researchers can then analyze results and determine if this particular variable alone causes increased participation.


Transferability: Applying Results of Experiments
Back to Relevance to English Studies

Experimentation and quasi-experimentation allow for generating transferable results (see the reference unit on transferability) and accepting those results as being dependent upon experimental rigor. It is an effective alternative to generalizability, which is difficult to rely upon in educational research. English scholars, reading results of experiments with a critical eye, ultimately decide if results will be implemented and how. They may even extend that existing research by replicating experiments in the interest of generating new results and benefiting from multiple perspectives. These results will strengthen the study or discredit findings.


Concerns English Scholars Express about Experiments
Back to Relevance to English Studies

Experimentation will not result in success in every situation. If there is no correlation between the dependent and independent variable, validity is impossible. Some transferability does exist in all experimental and educational research, but English scholars must ascertain the probability of results, given the issues at hand and questions asked to arrive at conclusions.

More specifically, researchers should carefully consider if a particular method is feasible in humanities studies, and whether it will yield the desired information. Some researchers recommend addressing pertinent issues combining several research methods available: survey, interview, ethnography, case study, content analysis, and experimentation (Lauer and Asher, 1988).


Advantages and Disadvantages of Experimental Research: Discussion
In educational research, experimentation is a way to gain insight into methods of instruction. Although teaching is context specific, results can provide a starting point for further study. Often, a teacher/researcher will have a "gut" feeling about an issue which can be explored through experimentation and looking at causal relationships. Through research intuition can shape practice.

A preconception exists that information obtained through scientific method is free of human inconsistencies. But, since scientific method is a matter of human construction, it is subject to human error. The researcher's Personal bias may intrude upon the experiment, as well. For example, certain preconceptions may dictate the course of the research and affect the behavior of the subjects. The issue may be compounded when, although many researchers are aware of the affect that their personal bias exerts on their own research, they are pressured to produce research that is accepted in their field of study as "legitimate" experimental research.

The researcher does bring bias to experimentation, but bias does not limit an ability to be reflective. An ethical researcher thinks critically about results and reports those results after careful reflection. Concerns over bias can be leveled against any research method.

Often, the sample may not be representative of a population, because the researcher does not have an opportunity to ensure a representative sample. For example, subjects could be limited to one location, limited in number, studied under constrained conditions and for too short a time.

Despite such inconsistencies in educational research, the researcher has control over the variables, increasing the possibility of more precisely determining individual effects of each variable. Also, determining interaction between variables is more possible.

Even so, artificial results may result. It can be argued that variables are manipulated so the experiment measures what researchers want to examine; therefore, the results are merely contrived products and have no bearing in material reality. Artificial results are difficult to apply in practical situations, making generalizing from the results of a controlled study questionable. Experimental research essentially first decontextualizes a single question from a "real world" scenario, studies it under controlled conditions, and then tries to recontextualize the results back on the "real world" scenario. Results may be difficult to replicate.

Perhaps, groups in an experiment may not be comparable. Quasi-experimentation in educational research is widespread because not only are many researchers also teachers, but many subjects are also students. With the classroom as laboratory, it is difficult to implement randomizing or matching strategies. Often, students self-select into certain sections of a course on the basis of their own agendas and scheduling needs. Thus when, as often happens, one class is treated and the other used for a control, the groups may not actually be comparable. As one might imagine, people who register for a class which meets three times a week at eleven o'clock in the morning (young, no full-time job, night people) differ significantly from those who register for one on Monday evenings from seven to ten p.m. (older, full-time job, possibly more highly motivated). Each situation presents different variables and your group might be completely different from that in the study. Long-term studies are expensive and hard to reproduce. And although often the same hypotheses are tested by different researchers, various factors complicate attempts to compare or synthesize them. It is nearly impossible to be as rigorous as the natural sciences model dictates.

Even when randomization of students is possible, problems arise. First, depending on the class size and the number of classes, the sample may be too small for the extraneous variables to cancel out. Second, the study population is not strictly a sample, because the population of students registered for a given class at a particular university is obviously not representative of the population of all students at large. For example, students at a suburban private liberal-arts college are typically young, white, and upper-middle class. In contrast, students at an urban community college tend to be older, poorer, and members of a racial minority. The differences can be construed as confounding variables: the first group may have fewer demands on its time, have less self-discipline, and benefit from superior secondary education. The second may have more demands, including a job and/or children, have more self-discipline, but an inferior secondary education. Selecting a population of subjects which is representative of the average of all post-secondary students is also a flawed solution, because the outcome of a treatment involving this group is not necessarily transferable to either the students at a community college or the students at the private college, nor are they universally generalizable.

When a human population is involved, experimental research becomes concerned if behavior can be predicted or studied with validity. Human response can be difficult to measure. Human behavior is dependent on individual responses. Rationalizing behavior through experimentation does not account for the process of thought, making outcomes of that process fallible (Eisenberg, 1996).

Nevertheless, we perform experiments daily anyway. When we brush our teeth every morning, we are experimenting to see if this behavior will result in fewer cavities. We are relying on previous experimentation and we are transferring the experimentation to our daily lives.

Moreover, experimentation can be combined with other research methods to ensure rigor. Other qualitative methods such as case study, ethnography, observational research and interviews can function as preconditions for experimentation or conducted simultaneously to add validity to a study.

We have few alternatives to experimentation. Mere anecdotal research, for example is unscientific, unreplicatable, and easily manipulated. Should we rely on Ed walking into a faculty meeting and telling the story of Sally? Sally screamed, "I love writing!" ten times before she wrote her essay and produced a quality paper. Therefore, all the other faculty members should hear this anecdote and know that all other students should employ this similar technique.

On final disadvantage: frequently, political pressure drives experimentation and forces unreliable results. Specific funding and support may drive the outcomes of experimentation and cause the results to be skewed. The reader of these results may not be aware of these biases and should approach experimentation with a critical eye.


Advantages and Disadvantages of Experimental Research: Quick Reference List
Experimental and quasi-experimental research can be summarized in terms of their advantages and disadvantages. This section combines and elaborates upon many points mentioned previously in this guide.

Advantages
Disadvantages

gain insight into methods of instruction
subject to human error

intuitive practice shaped by research
personal bias of researcher may intrude

teachers have bias but can be reflective
sample may not be representative

researcher can have control over variables
can produce artificial results

humans perform experiments anyway
results may only apply to one situation and may be difficult to replicate

can be combined with other research methods for rigor
groups may not be comparable

use to determine what is best for population
human response can be difficult to measure

provides for greater transferability than anecdotal research
political pressure may skew results



Experimental and Quasi-Experimental Research
Experimental research may be manipulated on both ends of the spectrum: by researcher and by reader. Researchers who report on experimental research, faced with naive readers of experimental research, encounter ethical concerns. While they are creating an experiment, certain objectives and intended uses of the results might drive and skew it. Looking for specific results, they may ask questions and look at data that support only desired conclusions. Conflicting research findings are ignored as a result. Similarly, researchers, seeking support for a particular plan, look only at findings which support that goal, dismissing conflicting research.

Editors and journals do not publish only trouble-free material. As readers of experiments members of the press might report selected and isolated parts of a study to the public, essentially transferring that data to the general population which may not have been intended by the researcher. Take, for example, oat bran. A few years ago, the press reported how oat bran reduces high blood pressure by reducing cholesterol. But that bit of information was taken out of context. The actual study found that when people ate more oat bran, they reduced their intake of saturated fats high in cholesterol. People started eating oat bran muffins by the ton, assuming a causal relationship when in actuality a number of confounding variables might influence the causal link.

Ultimately, ethical use and reportage of experimentation should be addressed by researchers, reporters and readers alike.

Reporters of experimental research often seek to recognize their audience's level of knowledge and try not to mislead readers. And readers must rely on the author's skill and integrity to point out errors and limitations. The relationship between researcher and reader may not sound like a problem, but after spending months or years on a project to produce no significant results, it may be tempting to manipulate the data to show significant results in order to jockey for grants and tenure.

Meanwhile, the reader may uncritically accept results that receive validity by being published in a journal. However, research that lacks credibility often is not published; consequentially, researchers who fail to publish run the risk of being denied grants, promotions, jobs, and tenure. While few researchers are anything but earnest in their attempts to conduct well-designed experiments and present the results in good faith, rhetorical considerations often dictate a certain minimization of methodological flaws.

Concerns arise if researchers do not report all, or otherwise alter, results. This phenomenon is counterbalanced, however, in that professionals are also rewarded for publishing critiques of others' work. Because the author of an experimental study is in essence making an argument for the existence of a causal relationship, he or she must be concerned not only with its integrity, but also with its presentation. Achieving persuasiveness in any kind of writing involves several elements: choosing a topic of interest, providing convincing evidence for one's argument, using tone and voice to project credibility, and organizing the material in a way that meets expectations for a logical sequence. Of course, what is regarded as pertinent, accepted as evidence, required for credibility, and understood as logical varies according to context. If the experimental researcher hopes to make an impact on the community of professionals in their field, she must attend to the standards and orthodoxy's of that audience.


Experimental and Quasi-Experimental Research: Related Links
Research Design Explained.
This site provides text of a "user-friendly" book that offers practical advice to students about how to read, conduct, and write up research. It is goal-approached, composed of chapters that cover major aspects of research including hypothesis, selecting measures, reading journal articles, writing research, and conducting research. It includes a sample paper, and appendix on how to use the library and another on how to conduct a study.


Inference Find: the Intelligent Fast parallel Web Search Engine.
This engine is included in this list to ensure that the reader is aware of its excellent ability to locate information on experimental and quasi-experimental research. Just type in the query term "experimental research design" and go from there. A significant service is provided by a fairly good ability to locate and organize information on research in various disciplines. It includes governmental and global sites, as well.


Contrasts: Traditional and computer-supported writing classrooms.
This Web presents a discussion of the Transitions Study, a year-long exploration of teachers and students in computer-supported and traditional writing classrooms. Includes description of study, rationale for conducting the study, results and implications of the study.



Experimental and Quasi-Experimental Research: Annotated Bibliography

A cozy world of trivial pursuits? (1996, June 28) The Times Educational Supplement. 4174, pp. 14-15.

A critique discounting the current methods Great Britain employs to fund and disseminate educational research. The belief is that research is performed for fellow researchers not the teaching public and implications for day to day practice are never addressed.
Anderson, J. A. (1979, Nov. 10-13). Research as argument: the experimental form. Paper presented at the annual meeting of the Speech Communication Association, San Antonio, TX.

In this paper, the scientist who uses the experimental form does so in order to explain that which is verified through prediction.
Anderson, Linda M. (1979). Classroom-based experimental studies of teaching effectiveness in elementary schools. (Technical Report UTR&D-R- 4102). Austin: Research and Development Center for Teacher Education, University of Texas.

Three recent large-scale experimental studies have built on a database established through several correlational studies of teaching effectiveness in elementary school.
Asher, J. W. (1976). Educational research and evaluation methods. Boston: Little, Brown.

Abstract unavailable by press time.
Babbie, Earl R. (1979). The Practice of Social Research. Belmont, CA: Wadsworth.

A textbook containing discussions of several research methodologies used in social science research.
Bangert-Drowns, R.L. (1993). The word processor as instructional tool: a meta-analysis of word processing in writing instruction. Review of Educational Research, 63 (1), 69-93.

Beach, R. (1993). The effects of between-draft teacher evaluation versus student self-evaluation on high school students' revising of rough drafts. Research in the Teaching of English, 13, 111-119.

The question of whether teacher evaluation or guided self-evaluation of rough drafts results in increased revision was addressed in Beach's study. Differences in the effects of teacher evaluations, guided self-evaluation (using prepared guidelines,) and no evaluation of rough drafts were examined. The final drafts of students (10th, 11th, and 12th graders) were compared with their rough drafts and rated by judges according to degree of change.
Beishuizen, J. & Moonen, J. (1992). Research in technology enriched schools: a case for cooperation between teachers and researchers. (ERIC Technical Report ED351006).

This paper describes the research strategies employed in the Dutch Technology Enriched Schools project to encourage extensive and intensive use of computers in a small number of secondary schools, and to study the effects of computer use on the classroom, the curriculum, and school administration and management.
Borg, W. P. (1989). Educational Research: an Introduction. (5th ed.). New York: Longman.

An overview of educational research methodology, including literature review and discussion of approaches to research, experimental design, statistical analysis, ethics, and rhetorical presentation of research findings.
Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Boston: Houghton Mifflin.

A classic overview of research designs.
Campbell, D.T. (1988). Methodology and epistemology for social science: selected papers. ed. E. S. Overman. Chicago: University of Chicago Press.

This is an overview of Campbell's 40-year career and his work. It covers in seven parts measurement, experimental design, applied social experimentation, interpretive social science, epistemology and sociology of science. Includes an extensive bibliography.
Caporaso, J. A., & Roos, Jr., L. L. (Eds.). Quasi-experimental approaches: Testing theory and evaluating policy. Evanston, WA: Northwestern University Press.

A collection of articles concerned with explicating the underlying assumptions of quasi-experimentation and relating these to true experimentation. With an emphasis on design. Includes a glossary of terms.
Collier, R. Writing and the word processor: How wary of the gift-giver should we be? Unpublished manuscript.

Unpublished typescript. Charts the developments to date in computers and composition and speculates about the future within the framework of Willie Sypher's model of the evolution of creative discovery.
Cook, T.D. & Campbell, D.T. (1979). Quasi-experimentation: design and analysis issues for field settings. Boston: Houghton Mifflin Co.

The authors write that this book "presents some quasi-experimental designs and design features that can be used in many social research settings. The designs serve to probe causal hypotheses about a wide variety of substantive issues in both basic and applied research."
Cutler, A. (1970). An experimental method for semantic field study. Linguistic Communication, 2, N. pag.

This paper emphasizes the need for empirical research and objective discovery procedures in semantics, and illustrates a method by which these goals may be obtained.
Daniels, L. B. (1996, Summer). Eisenberg's Heisenberg: The indeterminancies of rationality. Curriculum Inquiry, 26, 181-92.

Places Eisenberg's theories in relation to the death of foundationalism by showing that he distorts rational studies into a form of relativism. He looks at Eisenberg's ideas on indeterminacy, methods and evidence, what he is against and what we should think of what he says.
Danziger, K. (1990). Constructing the subject: Historical origins of psychological research. Cambridge: Cambridge University Press.

Danzinger stresses the importance of being aware of the framework in which research operates and of the essentially social nature of scientific activity.
Diener, E., et al. (1972, December). Leakage of experimental information to potential future subjects by debriefed subjects. Journal of Experimental Research in Personality, 264-67.

Research regarding research: an investigation of the effects on the outcome of an experiment in which information about the experiment had been leaked to subjects. The study concludes that such leakage is not a significant problem.
Dudley-Marling, C., & Rhodes, L. K.. (1989). Reflecting on a close encounter with experimental research. Canadian Journal of English Language Arts. 12, 24-28.

Researchers, Dudley-Marling and Rhodes, address some problems they met in their experimental approach to a study of reading comprehension. This article discusses the limitations of experimental research, and presents an alternative to experimental or quantitative research.
Edgington, E. S. (1985). Random assignment and experimental research. Educational Administration Quarterly, 21, N. pag.

Edgington explores ways on which random assignment can be a part of field studies. The author discusses both non-experimental and experimental research and the need for using random assignment.
Eisenberg, J. (1996, Summer). Response to critiques by R. Floden, J. Zeuli, and L. Daniels. Curriculum Inquiry, 26, 199-201.

A response to critiques of his argument that rational educational research methods are at best suspect and at worst futile. He believes indeterminacy controls this method and worries that chaotic research is failing students.
Eisner, E. (1992, July). Are all causal claims positivistic? A reply to Francis Schrag. Educational Researcher, 21(5), 8-9.

Eisner responds to Schrag who claimed that critics like Eisner cannot escape a positivistic paradigm whatever attempts they make to do so. Eisner argues that Schrag essentially misses the point for trying to argue for the paradigm solely on the basis of cause and effect without including the rest of positivistic philosophy. This weakens his argument against multiple modal methods, which Eisner argues provides opportunities to apply the appropriate research design where it is most applicable.
Floden, R.E. (1996, Summer). Educational research: limited, but worthwhile and maybe a bargain. (response to J.A. Eisenberg). Curriculum Inquiry, 26, 193-7.

Responds to John Eisenberg critique of educational research by asserting the connection between improvement of practice and research results. He places high value of teacher discrepancy and knowledge that research informs practice.
Fortune, J. C., & Hutson, B. A. (1994, March/April). Selecting models for measuring change when true experimental conditions do not exist. Journal of Educational Research, 197-206.

This article reviews methods for minimizing the effects of nonideal experimental conditions by optimally organizing models for the measurement of change.
Fox, R. F. (1980). Treatment of writing apprehension and tts effects on composition. Research in the Teaching of English, 14, 39-49.

The main purpose of Fox's study was to investigate the effects of two methods of teaching writing on writing apprehension among entry level composition students, A conventional teaching procedure was used with a control group, while a workshop method was employed with the treatment group.
Gadamer, H-G. (1976). Philosophical hermeneutics. (D. E. Linge, Trans.). Berkeley, CA: University of California Press.

A collection of essays with the common themes of the mediation of experience through language, the impossibility of objectivity, and the importance of context in interpretation.
Gaise, S. J. (1981). Experimental vs. non-experimental research on classroom second language learning. Bilingual Education Paper Series, 5, N. pag.

Aims on classroom-centered research on second language learning and teaching are considered and contrasted with the experimental approach.
Giordano, G. (1983). Commentary: Is experimental research snowing us? Journal of Reading, 27, 5-7.

Do educational research findings actually benefit teachers and students? Giordano states his opinion that research may be helpful to teaching, but is not essential and often is unnecessary.
Goldenson, D. R. (1978, March). An alternative view about the role of the secondary school in political socialization: A field-experimental study of theory and research in social education. Theory and Research in Social Education, 44-72.

This study concludes that when political discussion among experimental groups of secondary school students is led by a teacher, the degree to which the students' views were impacted is proportional to the credibility of the teacher.
Grossman, J., and J. P. Tierney. (1993, October). The fallibility of comparison groups. Evaluation Review, 556-71.

Grossman and Tierney present evidence to suggest that comparison groups are not the same as nontreatment groups.
Harnisch, D. L. (1992). Human judgment and the logic of evidence: A critical examination of research methods in special education transition literature. In D. L. Harnisch et al. (Eds.), Selected readings in transition.

This chapter describes several common types of research studies in special education transition literature and the threats to their validity.
Hawisher, G. E. (1989). Research and recommendations for computers and composition. In G. Hawisher and C. Selfe. (Eds.), Critical Perspectives on Computers and Composition Instruction. (pp. 44-69). New York: Teacher's College Press.

An overview of research in computers and composition to date. Includes a synthesis grid of experimental research.
Hillocks, G. Jr. (1982). The interaction of instruction, teacher comment, and revision in teaching the composing process. Research in the Teaching of English, 16, 261-278.

Hillock conducted a study using three treatments: observational or data collecting activities prior to writing, use of revisions or absence of same, and either brief or lengthy teacher comments to identify effective methods of teaching composition to seventh and eighth graders.
Jenkinson, J. C. (1989). Research design in the experimental study of intellectual disability. International Journal of Disability, Development, and Education, 69-84.

This article catalogues the difficulties of conducting experimental research where the subjects are intellectually disables and suggests alternative research strategies.
Jones, R. A. (1985). Research Methods in the Social and Behavioral Sciences. Sunderland, MA: Sinauer Associates, Inc..

A textbook designed to provide an overview of research strategies in the social sciences, including survey, content analysis, ethnographic approaches, and experimentation. The author emphasizes the importance of applying strategies appropriately and in variety.
Kamil, M. L., Langer, J. A., & Shanahan, T. (1985). Understanding research in reading and writing. Newton, Massachusetts: Allyn and Bacon.

Examines a wide variety of problems in reading and writing, with a broad range of techniques, from different perspectives.
Kennedy, J. L. (1985). An Introduction to the Design and Analysis of Experiments in Behavioral Research. Lanham, MD: University Press of America.

An introductory textbook of psychological and educational research.
Keppel, G. (1991). Design and analysis: a researcher's handbook. Englewood Cliffs, NJ: Prentice Hall.

This updates Keppel's earlier book subtitled "a student's handbook." Focuses on extensive information about analytical research and gives a basic picture of research in psychology. Covers a range of statistical topics. Includes a subject and name index, as well as a glossary.
Knowles, G., Elija, R., & Broadwater, K. (1996, Spring/Summer). Teacher research: enhancing the preparation of teachers? Teaching Education, 8, 123-31.

Researchers looked at one teacher candidate who participated in a class which designed their own research project correlating to a question they would like answered in the teaching world. The goal of the study was to see if preservice teachers developed reflective practice by researching appropriate classroom contexts.
Lace, J., & De Corte, E. (1986, April 16-20). Research on media in western Europe: A myth of sisyphus? Paper presented at the annual meeting of the American Educational Research Association. San Francisco.

Identifies main trends in media research in western Europe, with emphasis on three successive stages since 1960: tools technology, systems technology, and reflective technology.
Latta, A. (1996, Spring/Summer). Teacher as researcher: selected resources. Teaching Education, 8, 155-60.

An annotated bibliography on educational research including milestones of thought, practical applications, successful outcomes, seminal works, and immediate practical applications.
Lauer. J.M. & Asher, J. W. (1988). Composition research: Empirical designs. New York: Oxford University Press.

Approaching experimentation from a humanist's perspective to it, authors focus on eight major research designs: Case studies, ethnographies, sampling and surveys, quantitative descriptive studies, measurement, true experiments, quasi-experiments, meta-analyses, and program evaluations. It takes on the challenge of bridging language of social science with that of the humanist. Includes name and subject indexes, as well as a glossary and a glossary of symbols.
Mishler, E. G. (1979). Meaning in context: Is there any other kind? Harvard Educational Review, 49, 1-19.

Contextual importance has been largely ignored by traditional research approaches in social/behavioral sciences and in their application to the education field. Developmental and social psychologists have increasingly noted the inadequacies of this approach. Drawing examples for phenomenology, sociolinguistics, and ethnomethodology, the author proposes alternative approaches for studying meaning in context.
Mitroff, I., & Bonoma, T. V. (1978, May). Psychological assumptions, experimentations, and real world problems: A critique and an alternate approach to evaluation. Evaluation Quarterly, 235-60.

The authors advance the notion of dialectic as a means to clarify and examine the underlying assumptions of experimental research methodology, both in highly controlled situations and in social evaluation.
Muller, E. W. (1985). Application of experimental and quasi-experimental research designs to educational software evaluation. Educational Technology, 25, 27-31.

Muller proposes a set of guidelines for the use of experimental and quasi-experimental methods of research in evaluating educational software. By obtaining empirical evidence of student performance, it is possible to evaluate if programs are making the desired learning effect.
Murray, S., et al. (1979, April 8-12). Technical issues as threats to internal validity of experimental and quasi-experimental designs. San Francisco: University of California.

The article reviews three evaluation models and analyzes the flaws common to them. Remedies are suggested.
Muter, P., & Maurutto, P. (1991). Reading and skimming from computer screens and books: The paperless office revisited? Behavior and Information Technology, 10(4), 257-66.

The researchers test for reading and skimming effectiveness, defined as accuracy combined with speed, for written text compared to text on a computer monitor. They conclude that, given optimal on-line conditions, both are equally effective.
O'Donnell, A., Et al. (1992). The impact of cooperative writing. In J. R. Hayes, et al. (Eds.). Reading empirical research studies: The rhetoric of research. (pp. 371-84). Hillsdale, NJ: Lawrence Erlbaum Associates.

A model of experimental design. The authors investigate the efficacy of cooperative writing strategies, as well as the transferability of skills learned to other, individual writing situations.
Palmer, D. (1988). Looking at philosophy. Mountain View, CA: Mayfield Publishing.

An introductory text with incisive but understandable discussions of the major movements and thinkers in philosophy from the Pre-Socratics through Sartre. With illustrations by the author. Includes a glossary.
Phelps-Gunn, T., & Phelps-Terasaki, D. (1982). Written language instruction: Theory and remediation. London: Aspen Systems Corporation.

The lack of research in written expression is addressed and an application on the Total Writing Process Model is presented.
Poetter, T. (1996, Spring/Summer). From resistance to excitement: becoming qualitative researchers and reflective practitioners. Teaching Education, 8109-19.

An education professor reveals his own problematic research when he attempted to institute a educational research component to a teacher preparation program. He encountered dissent from students and cooperating professionals and ultimately was rewarded with excitement towards research and a recognized correlation to practice.
Purves, A. C. (1992). Reflections on research and assessment in written composition. Research in the Teaching of English, 26.

Three issues concerning research and assessment is writing are discussed: 1) School writing is a matter of products not process, 2) school writing is an ill-defined domain, 3) the quality of school writing is what observers report they see. Purves discusses these issues while looking at data collected in a ten-year study of achievement in written composition in fourteen countries.
Rathus, S. A. (1987). Psychology. (3rd ed.). Poughkeepsie, NY: Holt, Rinehart, and Winston.

An introductory psychology textbook. Includes overviews of the major movements in psychology, discussions of prominent examples of experimental research, and a basic explanation of relevant physiological factors. With chapter summaries.
Reiser, R. A. (1982). Improving the research skills of instructional designers. Educational Technology, 22, 19-21.

In his paper, Reiser starts by stating the importance of research in advancing the field of education, and points out that graduate students in instructional design lack the proper skills to conduct research. The paper then goes on to outline the practicum in the Instructional Systems Program at Florida State University which includes: 1) Planning and conducting an experimental research study; 2) writing the manuscript describing the study; 3) giving an oral presentation in which they describe their research findings.
Report on education research. (Journal). Washington, DC: Capitol Publication, Education News Services Division.

This is an independent bi-weekly newsletter on research in education and learning. It has been publishing since Sept. 1969.
Rossell, C. H. (1986). Why is bilingual education research so bad?: Critique of the Walsh and Carballo study of Massachusetts bilingual education programs. Boston: Center for Applied Social Science, Boston University. (ERIC Working Paper 86-5).

The Walsh and Carballo evaluation of the effectiveness of transitional bilingual education programs in five Massachusetts communities has five flaws and the five flaws are discussed in detail.
Rubin, D. L., & Greene, K. (1992). Gender-typical style in written language. Research in the Teaching of English, 26.

This study was designed to find out whether the writing styles of men and women differ. Rubin and Green discuss the pre-suppositions that women are better writers than men.
Sawin, E. (1992). Reaction: Experimental research in the context of other methods. School of Education Review, 4, 18-21.

Sawin responds to Gage's article on methodologies and issues in educational research. He agrees with most of the article but suggests the concept of scientific should not be regarded in absolute terms and recommends more emphasis on scientific method. He also questions the value of experiments over other types of research.
Schoonmaker, W. E. (1984). Improving classroom instruction: A model for experimental research. The Technology Teacher, 44, 24-25.

The model outlined in this article tries to bridge the gap between classroom practice and laboratory research, using what Schoonmaker calls active research. Research is conducted in the classroom with the students and is used to determine which two methods of classroom instruction chosen by the teacher is more effective.
Schrag, F. (1992). In defense of positivist research paradigms. Educational Researcher, 21, (5), 5-8.

The controversial defense of the use of positivistic research methods to evaluate educational strategies; the author takes on Eisner, Erickson, and Popkewitz.
Smith, J. (1997). The stories educational researchers tell about themselves. Educational Researcher, 33(3), 4-11.

Recapitulates main features of an on-going debate between advocates for using vocabularies of traditional language arts and whole language in educational research. An "impasse" exists were advocates "do not share a theoretical disposition concerning both language instruction and the nature of research," Smith writes (p. 6). He includes a very comprehensive history of the debate of traditional research methodology and qualitative methods and vocabularies. Definitely worth a read by graduates.
Smith, N. L. (1980). The feasibility and desirability of experimental methods in evaluation. Evaluation and Program Planning: An International Journal, 251-55.

Smith identifies the conditions under which experimental research is most desirable. Includes a review of current thinking and controversies.
Stewart, N. R., & Johnson, R. G. (1986, March 16-20). An evaluation of experimental methodology in counseling and counselor education research. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.

The purpose of this study was to evaluate the quality of experimental research in counseling and counselor education published from 1976 through 1984.
Spector, P. E. (1990). Research Designs. Newbury Park, California: Sage Publications.

In this book, Spector introduces the basic principles of experimental and nonexperimental design in the social sciences.
Tait, P. E. (1984). Do-it-yourself evaluation of experimental research. Journal of Visual Impairment and Blindness, 78, 356-363.

Tait's goal is to provide the reader who is unfamiliar with experimental research or statistics with the basic skills necessary for the evaluation of research studies.
Walsh, S. M. (1990). The current conflict between case study and experimental research: A breakthrough study derives benefits from both. (ERIC Document Number ED339721).

This paper describes a study that was not experimentally designed, but its major findings were generalizable to the overall population of writers in college freshman composition classes. The study was not a case study, but it provided insights into the attitudes and feelings of small clusters of student writers.
Waters, G. R. (1976). Experimental designs in communication research. Journal of Business Communication, 14.

The paper presents a series of discussions on the general elements of experimental design and the scientific process and relates these elements to the field of communication.
Welch, W. W. (March 1969). The selection of a national random sample of teachers for experimental curriculum evaluation. Scholastic Science and Math, 210-216.

Members of the evaluation section of Harvard project physics describe what is said to be the first attempt to select a national random sample of teachers, and list 6 steps to do so. Cost and comparison with a volunteer group are also discussed.
Winer, B.J. (1971). Statistical principles in experimental design, (2nd ed.). New York: McGraw-Hill.

Combines theory and application discussions to give readers a better understanding of the logic behind statistical aspects of experimental design. Introduces the broad topic of design, then goes into considerable detail. Not for light reading. Bring your aspirin if you like statistics. Bring morphine is you're a humanist.
Winn, B. (1986, January 16-21). Emerging trends in educational technology research. Paper presented at the Annual Convention of the Association for Educational Communication Technology.

This examination of the topic of research in educational technology addresses four major areas: (1) why research is conducted in this area and the characteristics of that research; (2) the types of research questions that should or should not be addressed; (3) the most appropriate methodologies for finding answers to research questions; and (4) the characteristics of a research report that make it good and ultimately suitable for publication.

Overview: Generalizability and Transferability

Generalizability and transferability refer to our efforts to compare the results of studies. To read more about these issues, click on the list below:

Introduction
Generalizability
Transferability
Synthesis
Applications to Research Methods
Qualitative vs. Quantitative Debate
Key Terms
Annotated Bibliography
Contributors to this Guide

Introduction to Generalizability and Transferability
In this chapter, we discuss generalizabililty, transferability, and the interrelationship between the two. We also explain how these two aspects of research operate in different methodologies, demonstrating how researchers may apply these concepts throughout the research process.

Generalizability Overview
Transferability Overview
Interrelationships


Generalizability Overview

Generalizability is applied by researchers in an academic setting. It can be defined as the extension of research findings and conclusions from a study conducted on a sample population to the population at large. While the dependability of this extension is not absolute, it is statistically probable. Because sound generalizability requires data on large populations, quantitative research -- experimental for instance -- provides the best foundation for producing broad generalizability. The larger the sample population, the more one can generalize the results. For example, a comprehensive study of the role computers play in the writing process might reveal that it is statistically probable that students who do most of their composing on a computer will move chunks of text around more than students who do not compose on a computer.


Transferability Overview

Transferability is applied by the readers of research. Although generalizability usually applies only to certain types of quantitative methods, transferability can apply in varying degrees to most types of research . Unlike generalizability, transferability does not involve broad claims, but invites readers of research to make connections between elements of a study and their own experience. For instance, teachers at the high school level might selectively apply to their own classrooms results from a study demonstrating that heuristic writing exercises help students at the college level.


Generalizability and Transferability: Interrelationships

Generalizability and transferability are important elements of any research methodology, but they are not mutually exclusive: generalizability, to varying degrees, rests on the transferability of research findings. It is important for researchers to understand the implications of these twin aspects of research before designing a study. Researchers who intend to make a generalizable claim must carefully examine the variables involved in the study. Among these are the sample of the population used and the mechanisms behind formulating a causal model. Furthermore, if researchers desire to make the results of their study transferable to another context, they must keep a detailed account of the environment surrounding their research, and include a rich description of that environment in their final report. Armed with the knowledge that the sample population was large and varied, as well as with detailed information about the study itself, readers of research can more confidently generalize and transfer the findings to other situations.


Generalizability
Generalizability is not only common to research, but to everyday life as well. In this section, we establish a practical working definition of generalizability as it is applied within and outside of academic research. We also define and consider three different types of generalizability and some of their probable applications. Finally, we discuss some of the possible shortcomings and limitations of generalizability that researchers must be aware of when constructing a study they hope will yield potentially generalizable results.

Definition
Example
Considerations
Potential Limitations

Generalizability: Definition

In many ways, generalizability amounts to nothing more than making predictions based on a recurring experience. If something occurs frequently, we expect that it will continue to do so in the future. Researchers use the same type of reasoning when generalizing about the findings of their studies. Once researchers have collected sufficient data to support a hypothesis, a premise regarding the behavior of that data can be formulated, making it generalizable to similar circumstances. Because of its foundation in probability, however, such a generalization cannot be regarded as conclusive or exhaustive.

While generalizability can occur in informal, nonacademic settings, it is usually applied only to certain research methods in academic studies. Quantitative methods allow some generalizability. Experimental research, for example, often produces generalizable results. However, such experimentation must be rigorous in order for generalizable results to be found.


Generalizability: Considerations

There are three types of generalizability that interact to produce probabilistic models. All of them involve generalizing a treatment or measurement to a population outside of the original study. Researchers who wish to generalize their claims should try to apply all three forms to their research, or the strength of their claims will be weakened (Runkel & McGrath, 1972).

In one type of generalizability, researchers determine whether a specific treatment will produce the same results in different circumstances. To do this, they must decide if an aspect within the original environment, a factor beyond the treatment, generated the particular result. This will establish how flexibly the treatment adapts to new situations. Higher adaptability means that the treatment is generalizable to a greater variety of situations. For example, imagine that a new set of heuristic prewriting questions designed to encourage freshman college students to consider audience more fully works so well that the students write thoroughly developed rhetorical analyses of their target audiences. To responsibly generalize that this heuristic is effective, a researcher would need to test the same prewriting exercise in a variety of educational settings at the college level, using different teachers, students, and environments. If the same positive results are produced, the treatment is generalizable.

A second form of generalizability focuses on measurements rather than treatments. For a result to be considered generalizable outside of the test group, it must produce the same results with different forms of measurement. In terms of the heuristic example above, the findings will be more generalizable if the same results are obtained when assessed "with questions having a slightly different wording, or when we use a six-point scale instead of a nine-point scale" (Runkel & McGrath, 1972, p.46).

A third type of generalizability concerns the subjects of the test situation. Although the results of an experiment may be internally valid, that is, applicable to the group tested, in many situations the results cannot be generalized beyond that particular group. Researchers who hope to generalize their results to a larger population should ensure that their test group is relatively large and randomly chosen. However, researchers should consider the fact that test populations of over 10,000 subjects do not significantly increase generalizability (Firestone,1993).


Generalizability: Potential Limitations

No matter how carefully these three forms of generalizability are applied, there is no absolute guarantee that the results obtained in a study will occur in every situation outside the study. In order to determine causal relationships in a test environment, precision is of utmost importance. Yet if researchers wish to generalize their findings, scope and variance must be emphasized over precision. Therefore, it becomes difficult to test for precision and generalizability simultaneously, since a focus on one reduces the reliability of the other. One solution to this problem is to perform a greater number of observations, which has a dual effect: first, it increases the sample population, which heightens generalizability; second, precision can be reasonably maintained because the random errors between observations will average out (Runkel and McGrath, 1972).


Transferability
Transferability describes the process of applying the results of research in one situation to other similar situations. In this section, we establish a practical working definition of transferability as it's applied within and outside of academic research. We also outline important considerations researchers must be aware of in order to make their results potentially transferable, as well as the critical role the reader plays in this process. Finally, we discuss possible shortcomings and limitations of transferability that researchers must be aware of when planning and conducting a study that will yield potentially transferable results.

Definition
Example
Considerations
Potential Limitations

Transferability: Definition

Transferability is a process performed by readers of research. Readers note the specifics of the research situation and compare them to the specifics of an environment or situation with which they are familiar. If there are enough similarities between the two situations, readers may be able to infer that the results of the research would be the same or similar in their own situation. In other words, they "transfer" the results of a study to another context. To do this effectively, readers need to know as much as possible about the original research situation in order to determine whether it is similar to their own. Therefore, researchers must supply a highly detailed description of their research situation and methods.

Results of any type of research method can be applied to other situations, but transferability is most relevant to qualitative research methods such as ethnography and case studies. Reports based on these research methods are detailed and specific. However, because they often consider only one subject or one group, researchers who conduct such studies seldom generalize the results to other populations. The detailed nature of the results, however, makes them ideal for transferability.


Transferability: Example

Transferability is easy to understand when you consider that we are constantly applying this concept to aspects of our daily lives. If, for example, you are an inexperienced composition instructor and you read a study in which a veteran writing instructor discovered that extensive prewriting exercises helped students in her classes come up with much more narrowly defined paper topics, you could ask yourself how much the instructor's classroom resembled your own. If there were many similarities, you might try to draw conclusions about how increasing the amount of prewriting your students do would impact their ability to arrive at sufficiently narrow paper topics. In doing so, you would be attempting to transfer the composition researcher's techniques to your own classroom.

An example of transferable research in the field of English studies is Berkenkotter, Huckin, and Ackerman's (1988) study of a graduate student in a rhetoric Ph.D. program. In this case study, the researchers describe in detail a graduate student's entrance into the language community of his academic program, and particularly his struggle learning the writing conventions of this community. They make conclusions as to why certain things might have affected the graduate student, "Nate," in certain ways, but they are unable to generalize their findings to all graduate students in rhetoric Ph.D. programs. It is simply one study of one person in one program. However, from the level of detail the researchers provide, readers can take certain aspects of Nate's experience and apply them to other contexts and situations. This is transferability. First-year graduate students who read the Berkenhotter, Huckin, and Ackerman study may recognize similarities in their own situation while professors may recognize difficulties their students are having and understand these difficulties a bit better. The researchers do not claim that their results apply to other situations. Instead, they report their findings and make suggestions about possible causes for Nate's difficulties and eventual success. Readers then look at their own situation and decide if these causes may or may not be relevant.


Transferability: Considerations

When designing a study researchers have to consider their goals: Do they want to provide limited information about a broad group in order to indicate trends or patterns? Or do they want to provide detailed information about one person or small group that might suggest reasons for a particular behavior? The method they choose will determine the extent to which their results can be transferred since transferability is more applicable to certain kinds of research methods than others.

Thick Description: When writing up the results of a study, it is important that the researcher provide specific information about and a detailed description of her subject(s), location, methods, role in the study, etc. This is commonly referred to as "thick description" of methods and findings; it is important because it allows readers to make an informed judgment about whether they can transfer the findings to their own situation. For example, if an educator conducts an ethnography of her writing classroom, and finds that her students' writing improved dramatically after a series of student-teacher writing conferences, she must describe in detail the classroom setting, the students she observed, and her own participation. If the researcher does not provide enough detail, it will be difficult for readers to try the same strategy in their own classrooms. If the researcher fails to mention that she conducted this research in a small, upper-class private school, readers may transfer the results to a large, inner-city public school expecting a similar outcome.

The Reader's Role: The role of readers in transferability is to apply the methods or results of a study to their own situation. In doing so, readers must take into account differences between the situation outlined by the researcher and their own. If readers of the Berkenhotter, Huckin, and Ackerman study discussed in the "Examples" section of this unit are aware that the research was conducted in a small, upper-class private school, but decide to test the method in a large inner-city public school, they must make adjustments for the different setting and be prepared for different results.

Likewise, readers may decide that the results of a study are not transferable to their own situation. For example, if a study found that watching more than 30 hours of television a week resulted in a worse GPA for graduate students in physics, graduate students in broadcast journalism may conclude that these results do not apply to them.

Readers may also transfer only certain aspects of the study and not the entire conclusion. For example, in the Berkenhotter, Huckin, and Ackerman study, the researchers suggest a variety of reasons for why the graduate student studied experienced difficulties adjusting to his Ph.D. program. Although composition instructors cannot compare "Nate" to first-year college students in their composition class, they could ask some of the same questions about their own class, offering them insight into some of the writing difficulties the first-year undergraduates are experiencing. It is up to readers to decide what findings are important and which may apply to their own situation; if researchers fulfill their responsibility to provide "thick description," this decision is much easier to make.


Transferability: Potential Limitations

Understanding research results can help us understand why and how something happens. However, many researchers believe that such understanding is difficult to achieve in relation to human behaviors which they contend are too difficult to understand and often impossible to predict. "Because of the many and varied ways in which individuals differ from each other and because these differences change over time, comprehensive and definitive experiments in the social sciences are not possible...the most we can ever realistically hope to achieve in educational research is not prediction and control but rather only temporary understanding" (Cziko, 1993, p. 10).

Cziko's point is important because transferability allows for "temporary understanding." Instead of applying research results to every situation that may occur in the future, we can apply a similar method to another, similar situation, observe the new results, apply a modified version to another situation, and so on. Transferability takes into account the fact that there are no absolute answers to given situations; rather, every individual must determine their own best practices. Transferring the results of research performed by others can help us develop and modify these practices. However, it is important for readers of research to be aware that results cannot always be transferred; a result that occurs in one situation will not necessarily occur in a similar situation. Therefore, it is critical to take into account differences between situations and modify the research process accordingly.

Although transferability seems to be an obvious, natural, and important method for applying research results and conclusions, it is not perceived as a valid research approach in some academic circles. Perhaps partly in response to critics, in many modern research articles, researchers refer to their results as generalizable or externally valid. Therefore, it seems as though they are not talking about transferability. However, in many cases those same researchers provide direction about what points readers may want to consider, but hesitate to make any broad conclusions or statements. These are characteristics of transferable results.

Generalizability is actually, as we have seen, quite different from transferability. Unfortunately, confusion surrounding these two terms can lead to misinterpretation of research results. Emphasis on the value of transferable results -- as well as a clear understanding among researchers in the field of English of critical differences between the conditions under which research can be generalized, transferred, or, in some cases, both generalized and transferred -- could help qualitative researchers avoid some of the criticisms launched by skeptics who question the value of qualitative research methods.


Generalizability and Transferability: Synthesis
Generalizability allows us to form coherent interpretations in any situation, and to act purposefully and effectively in daily life. Transferability gives us the opportunity to sort through given methods and conclusions to decide what to apply to our own circumstances. In essence, then, both generalizability and transferability allow us to make comparisons between situations. For example, we can generalize that most people in the United States will drive on the right side of the road, but we cannot transfer this conclusion to England or Australia without finding ourselves in a treacherous situation. It is important, therefore, to always consider context when generalizing or transferring results.

Whether a study emphasizes transferability or generalizability is closely related to the goals of the researcher and the needs of the audience. Studies done for a magazine such as Time or a daily newspaper tend towards generalizability, since the publishers want to provide information relevant to a large portion of the population. A research project pointed toward a small group of specialists studying a similar problem may emphasize transferability, since specialists in the field have the ability to transfer aspects of the study results to their own situations without overt generalizations provided by the researcher. Ultimately, the researcher's subject, audience, and goals will determine the method the researcher uses to perform a study, which will then determine the transferability or generalizability of the results.

Generalizability and Transferability: A Comparison
Generalizability and Transferability: Controversy, Worth, and Function

Generalizability and Transferability: A Comparison

Although generalizability has been a preferred method of research for quite some time, transferability is relatively a new idea. In theory, however, it has always accompanied research issues. It is important to note that generalizability and transferability are not necessarily mutually exclusive; they can overlap.

From an experimental study to a case study, readers transfer the methods, results, and ideas from the research to their own context. Therefore, a generalizable study can also be transferable. For example, a researcher may generalize the results of a survey of 350 people in a university to the university population as a whole; readers of the results may apply, or transfer, the results to their own situation. They will ask themselves, basically, if they fall into the majority or not. However, a transferable study is not always generalizable. For example, in case studies, transferability allows readers the option of applying results to outside contexts, whereas generalizability is basically impossible because one person or a small group of people is not necessarily representative of the larger population.


Generalizability and Transferability: Controversy, Worth, and Function

Research in the natural sciences has a long tradition of valuing empirical studies; experimental investigation has been considered "the" way to perform research. As social scientists adapted the methods of natural science research to their own needs, they adopted this preference for empirical research. Therefore, studies that are generalizable have long been thought to be more worthwhile; the value of research was often determined by whether a study was generalizable to a population as a whole. However, more and more social scientists are realizing the value of using a variety of methods of inquiry, and the value of transferability is being recognized.

It is important to recognize that generalizability and transferability do not alone determine a study's worth. They perform different functions in research, depending on the topic and goals of the researcher. Where generalizable studies often indicate phenomena that apply to broad categories such as gender or age, transferability can provide some of the how and why behind these results.

However, there are weaknesses that must be considered. Researchers can study a small group that is representative of a larger group and claim that it is likely that their results are applicable to the larger group, but it is impossible for them to test every single person in the larger group. Their conclusions, therefore, are only valid in relation to their own studies. Another problem is that a non-representative group can lead to a faulty generalization. For example, a study of composition students' revision capabilities which compared students' progress made during a semester in a computer classroom with progress exhibited by students in a tradtional classroom might show that computers do aid students in the overall composing process. However, if it were discovered later that an unsusually high number of students in the traditional classrooms suffered from substance abuse problems outside of the classroom, the population studied would not be considered representative of the student population as a whole. Therefore, it would be problematic to generalize the results of the study to a larger student population.

In the case of transferability, readers need to know as much detail as possible about a research situation in order to accurately transfer the results to their own. However, it is impossible to provide an absolutely complete description of a situation, and missing details may lead a reader to transfer results to a situation that is not entirely similar to the original one.


Applications to Research Methods
The degree to which generalizability and transferability are applicable differs from methodology to methodology as well as from study to study. Researchers need to be aware of these degrees so that results are not undermined by over-generalizations, and readers need to ensure that they do not read researched results in such a way that the results are misapplied or misinterpreted.

Application: Case Study
Applicaton: Ethnography
Application: Experimental Research
Application: Survey

Applications of Transferability and Generalizability: Case Study

Research Design
Case studies examine individuals or small groups within a specific context. Research is typically gathered through qualitative means: interviews, observations, etc. Data is usually analyzed either holistically or by coding methods.

Assumptions
In research involving case studies, a researcher typically assumes that the results will be transferable. Generalizing is difficult or impossible because one person or small group cannot represent all similar groups or situations. For example, one group of beginning writing students in a particular classroom cannot represent all beginning student writers. Also, conclusions drawn in case studies are only about the participants being observed. With rare exceptions, case studies are not meant to establish cause/effect relationships between variables. The results of a case study are transferable in that researchers "suggest further questions, hypotheses, and future implications," and present the results as "directions and questions" (Lauer & Asher 32).

Example
In order to illustrate the writing skills of beginning college writers, a researcher completing a case study might single out one or more students in a composition classroom and set about talking to them about how they judge their own writing as well as reading actual papers, setting up criteria for judgment, and reviewing paper grades/teacher interpretation.

Results of a Study
In presenting the results of the previous example, a researcher should define the criteria that were established in order to determine what the researcher meant by "writing skills," provide noteworthy quotes from student interviews, provide other information depending on the kinds of research methods used (e.g., surveys, classroom observation, collected writing samples), and include possibilities for furthering this type of research. Readers are then able to assess for themselves how the researcher's observations might be transferable to other writing classrooms.


Applications of Transferability and Generalizability: Ethnography

Research Design
Ethnographies study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through observer immersion into the culture or group. Research is completed through various methods, which are similar to those of case studies, but since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research. (Jonathon Kozol's "There Are No Children Here" is a good example of this.)

Assumptions
As with case studies, findings of ethnographies are also considered to be transferable. The main goals of an ethnography are to "identify, operationally define, and interrelate variables" within a particular context, which ultimately produce detailed accounts or "thick descriptions" (Lauer & Asher 39). Unlike a case study, the researcher here discovers many more details. Results of ethnographies should "suggest variables for further investigation" and not generalize beyond the participants of a study (Lauer & Asher 43). Also, since analysts completing this type of research tend to rely on multiple methods to collect information (a practice also referred to as triangulation), their results typically help create a detailed description of human behavior within a particular environment.

Example
The Iowa Writing Program has a widespread reputation for producing excellent writers. In order to begin to understand their training, an ethnographer might observe students throughout their degree program. During this time, the ethnographer could examine the curriculum, follow the writing processes of individual writers, and become acquainted with the writers and their work. By the end of a two year study, the researcher would have a much deeper understanding of the unique and effective features of the program.

Results of a Study
Obviously, the Iowa Writing Program is unique, so generalizing any results to another writing program would be problematic. However, an ethnography would provide readers with insights into the program. Readers could ask questions such as: what qualities make it strong and what is unique about the writers who are trained within the program? At this point, readers could attempt to "transfer" applicable knowledge and observations to other writing environments.


Applications of Transferability and Generalizability: Experimental Research
Back to Applications to Research Methods

Research Design
A researcher working within this methodology creates an environment in which to observe and interpret the results of a research question. A key element in experimental research is that participants in a study are randomly assigned to groups. In an attempt to create a causal model (i.e., to discover the causal origin of a particular phenomenon), groups are treated differently and measurements are conducted to determine if different treatments appear to lead to different effects.

Assumptions
Experimental research is usually thought to be generalizable. This methodology explores cause/effect relationships through comparisons among groups (Lauer & Asher 152). Since participants are randomly assigned to groups, and since most experiments involve enough individuals to reasonably approximate the populations from which individual participants are drawn, generalization is justified because "over a large number of allocations, all the groups of subjects will be expected to be identical on all variables" (155).

Example
A simplified example: Six composition classrooms are randomly chosen (as are the students and instructors) in which three instructors incorporate the use of electronic mail as a class activity and three do not. When students in the first three classes begin discussing their papers through e-mail and, as a result, make better revisions to their papers than students in the other three classes, a researcher is likely to conclude that incorporating e-mail within a writing classroom improves the quality of students' writing.

Results of a Study
Although experimental research is based on cause/effect relationships, "certainty" can never be obtained, but rather results are "probabilistic" (Lauer and Asher 161). Depending on how the researcher has presented the results, they are generalizable in that the students were selected randomly. Since the quality of writing improved with the use of e-mail within all three classrooms, it is probable that e-mail is the cause of the improvement. Readers of this study would transfer the results when they sorted out the details: Are these students representative of a group of students with which the reader is familiar? What types of previous writing experiences have these students had? What kind of writing was expected from these students? The researcher must have provided these details in order for the results to be transferable.


Applications of Transferability and Generalizability: Survey

Research Design
The goal of a survey is to gain specific information about either a specific group or a representative sample of a particular group. Survey respondents are asked to respond to one or more of the following kinds of items: open-ended questions, true-false questions, agree-disagree (or Likert) questions, rankings, ratings, and so on. Results are typically used to understand the attitudes, beliefs, or knowledge of a particular group.

Assumptions
Assuming that care has been taken in the development of the survey items and selection of the survey sample and that adequate response rates have been achieved, surveys results are generalizable. Note, however, that results from surveys should be generalized only to the population from which the survey results were drawn.

Example
For instance, a survey of Colorado State University English graduate students undertaken to determine how well French philosopher/critic Jacques Derrida is understood before and after students take a course in critical literary theory might inform professors that, overall, Derrida's concepts are understood and that CSU's literary theory class, E615, has helped students grasp Derrida's ideas.

Results of a Study
The generalizability of surveys depends on several factors. Whether distributed to a mass of people or a select few, surveys are of a "personal nature and subject to distortion." Survey respondents may or may not understand the questions being asked of them. Depending on whether or not the survey designer is nearby, respondents may or may not have the opportunity to clarify their misunderstandings.

It is also important to keep in mind that errors can occur at the development and processing levels. A researcher may inadequately pose questions (that is, not ask the right questions for the information being sought), disrupt the data collection (surveying certain people and not others), and distort the results during the processing (misreading responses and not being able to question the participant, etc.). One way to avoid these kinds of errors is for researchers to examine other studies of a similar nature and compare their results with results that have been obtained in previous studies. This way, any large discrepancies will be exposed. Depending on how large those discrepancies are and what the context of the survey is, the results may or may not be generalizable. For example, if an improved understanding of Derrida is apparent after students complete E615, it can be theorized that E615 effectively teaches students the concepts of Derrida. Issues of transferability might be visible in the actual survey questions themselves; that is, they could provide critical background information readers might need to know in order to transfer the results to another context.


The Qualitative versus Quantitative Debate
In Miles and Huberman's 1994 book Qualitative Data Analysis, quantitative researcher Fred Kerlinger is quoted as saying, "There's no such thing as qualitative data. Everything is either 1 or 0" (p. 40). To this another researcher, D. T. Campbell, asserts "all research ultimately has a qualitative grounding" (p. 40). This back and forth banter among qualitative and quantitative researchers is "essentially unproductive" according to Miles and Huberman. They and many other researchers agree that these two research methods need each other more often than not. However, because typically qualitative data involves words and quantitative data involves numbers, there are some researchers who feel that one is better (or more scientific) than the other. Another major difference between the two is that qualitative research is inductive and quantitative research is deductive. In qualitative research, a hypothesis is not needed to begin research. However, all quantitative research requires a hypothesis before research can begin.

Another major difference between qualitative and quantitative research is the underlying assumptions about the role of the researcher. In quantitative research, the researcher is ideally an objective observer that neither participates in nor influences what is being studied. In qualitative research, however, it is thought that the researcher can learn the most about a situation by participating and/or being immersed in it. These basic underlying assumptions of both methodologies guide and sequence the types of data collection methods employed.

Although there are clear differences between qualitative and quantitative approaches, some researchers maintain that the choice between using qualitative or quantitative approaches actually has less to do with methodologies than it does with positioning oneself within a particular discipline or research tradition. The difficulty of choosing a method is compounded by the fact that research is often affiliated with universities and other institutions. The findings of research projects often guide important decisions about specific practices and policies. The choice of which approach to use may reflect the interests of those conducting or benefitting from the research and the purposes for which the findings will be applied. Decisions about which kind of research method to use may also be based on the researcher's own experience and preference, the population being researched, the proposed audience for findings, time, money, and other resources available (Hathaway, 1995).

Some researchers believe that qualitative and quantitative methodologies cannot be combined because the assumptions underlying each tradition are so vastly different. Other researchers think they can be used in combination only by alternating between methods: qualitative research is appropriate to answer certain kinds of questions in certain conditions and quantitative is right for others. And some researchers think that both qualitative and quantitative methods can be used simultaneously to answer a research question.

To a certain extent, researchers on all sides of the debate are correct: each approach has its drawbacks. Quantitative research often "forces" responses or people into categories that might not "fit" in order to make meaning. Qualitative research, on the other hand, sometimes focuses too closely on individual results and fails to make connections to larger situations or possible causes of the results. Rather than discounting either approach for its drawbacks, though, researchers should find the most effective ways to incorporate elements of both to ensure that their studies are as accurate and thorough as possible.

It is important for researchers to realize that qualitative and quantitative methods can be used in conjunction with each other. In a study of computer-assisted writing classrooms, Snyder (1995) employed both qualitative and quantitative approaches. The study was constructed according to guidelines for quantitative studies: the computer classroom was the "treatment" group and the traditional pen and paper classroom was the "control" group. Both classes contained subjects with the same characteristics from the population sampled. Both classes followed the same lesson plan and were taught by the same teacher in the same semester. The only variable used was the computers. Although Snyder set this study up as an "experiment," she used many qualitative approaches to supplement her findings. She observed both classrooms on a regular basis as a participant-observer and conducted several interviews with the teacher both during and after the semester. However, there were several problems in using this approach: the strict adherence to the same syllabus and lesson plans for both classes and the restricted access of the control group to the computers may have put some students at a disadvantage. Snyder also notes that in retrospect she should have used case studies of the students to further develop her findings. Although her study had certain flaws, Snyder insists that researchers can simultaneously employ qualitative and quantitative methods if studies are planned carefully and carried out conscientiously.


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Argues that studies of teacher development will be more generalizable if a broad set of methods are used to collect data, if the data collected is both extensive and intensive, and if the methods used take into account the differences in people and situations being studied.
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Issues of consistency, triangulation, and generalizability are discussed in relation to a qualitative study involving graduate student participants. The authors refute Polkinghorne's views of the generalizability of qualitative research, arguing that quantitative research is more suitable for generalizability.
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This article explains a study in which the author employed quantitative and qualitative methods simultaneously to compare computer composition classrooms and traditional classrooms. Although there were some problems with integrating both approaches, Snyder says they can be used together if researchers plan carefully and use their methods thoughtfully.

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A small section on the application of generalizability in regards to case studies.