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FCE 3900PENYELIDIKAN PENDIDIKAN

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Rekabentuk Quasi-Eksperimen

The word "quasi" means as if or almost, so a quasi-experiment

means almost a true experiment.

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Kenapa Quasi-Eksperimen? Dinamakan Quasi-Eksperimental kerana penyelidikan yang

dilakukan hampir menyerupai reka bentuk eksperimen, kecuali tiada pembahagian secara rawak dilakukan.

Pembahagian rawak sukar dijalankan dalam keadaan tertentu seperti pemboleh ubah atribut (jantina, ras, pendapatan penjaga dll) kerana akan menjejaskan pemboleh ubah seperti rasa terasing dalam kumpulan baru dan sebagainya. Oleh itu reka bentuk ini digunakan.

Kaedah reka bentuk quasi-eksperimen bukan sebenar-benar eksperimen kerana tiada pembahagian rawak dilakukan.

Gunakan kumpulan sedia wujud dalam (intact group).

Quasi-experimental Design

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QUASI-EXPERIMENTAL DESIGNS

QUASI-EXPERIMENTAL DESIGNS; are usually constructions that already exist in the real world. Those designs that fall into the quasi-experimental category fall short in some way of the criteria for the true experimental group. A quasi-experimental design will have some sort of control and experimental group, but these groups probably weren't randomly selected. Random selection is usally where true-experimental and quasi-experimental designs differ.

Some advantages of the quasi-experimental design include:

• Greater external validity (more like real world conditions)

• Much more feasible given time and logistical constraints

Disadvantages:

• Not as many variables controlled (less causal claims)

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Ciri-Ciri Rekabentuk Quasi-Eksperimen

Dua atau lebih daripada kumpulan responden yang tidak diagih secara rawak.

Memerlukan kawalan terhadap pemboleh ubah luaran yang tegas.

Mengutamakan perbezaan antara kumpulan responden secara semula jadi.

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Perbezaan antara eksperimen dengan quasi-eksperimen.

Aktiviti bacaan: Sila baca artikel yang bertajuk;EXPERIMENTAL AND QUASI-EXPERIMENTAL RESEARCH DESIGN

True and Quasi-Experimental Designs.

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Causal-Comparative Studies

Important Issues: Primary purpose should be developing cause-

and-effect relationships when experimentation is not possible

The “intervention” must have already occurred Must identify and consider extraneous variables Differences between the groups not due to the

independent variable should be controlled Be careful with causal conclusions

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Causal-Comparative Studies

Go beyond relationships/associations to examine cause-and-effects.

Two types of these studies: Ex Post Facto Correlational

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Causal-Comparative StudiesEx Post Facto Applied when seeking cause-and-effect

relationships, but cannot do experiments One or more independent variables are used

to study their effects on one dependent variable

Ex: What is the impact of a particular training on job performance

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Causal-Comparative StudiesEx Post Facto (cont’d)Ex. Continued:

Approach 1: Select two groups, one group is trained, the other is not. Next their job performance is measured (experimental design)

Approach 2: Look for groups with pre-existing conditions and compare their job performance. e.g., select a group that is already trained and one that is not trained and compare their job performance.

The groups selected must be as close as possible except for the independent variable

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Causal-Comparative StudiesCorrelationalThese studies use more sophisticated versions of

correlation analysis to investigate cause-and-effects Path Analysis:

A causal model is developed from theory which shows by arrows the causal sequence that is expected. Correlation between these variables is used as empirical evidence of the proposed links.

Newer, more sophisticated methods: Structural Equation Modeling Latent Variable Causal Modeling

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Ex Post Facto

Ex post facto

‘from what is done afterwards’

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Ex post facto The Latin term "Ex post facto" means, in a

UK legal context: "by reason of a subsequent act".

Ex-post-facto designs ("after the fact")

From what is done afterwards

Studies that investigate possible cause and effect relationships by observing an existing condition or state of affairs and searching back in time for plausible causal factors.

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• Explores possible causes and effects. • The independent variable is not manipulated, it

has already been applied. • Focuses first on the effect, then attempts to

determine what caused the observed effect.

Ex Post Facto (Causal-Comparative) Research

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The ex post facto design is a variation of the "after-only with control group" experimental design. The chief difference is that both the

experimental and control groups are selected after the experimental variable is introduced rather than before. This approach eliminates

the possibility that participants will be influenced by an awareness that they are being

tested.

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This type of study is very common and useful when using human subjects in real-world

situations and the investigator comes in "after the fact." For example, it might be observed that

students from one town have higher grades than students from a different town attending the same

high school. Would just "being from a certain town" explain the differences? In an ex post facto study, specific reasons for the differences would

be explored, such as differences in income, ethnicity, parent support, etc.

Characteristics of Ex Post Facto

Researcher takes the effect/dependent variable and examines it retrospectively

Establishes causes, relationships or associations and their meanings.

Researcher has little to no control over independent variables.

Flexible by nature.

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• There is a control or comparison group. • Intact groups are used. • The treatment is not manipulated, it has

already occurred.

Characteristics of Ex Post Facto Research

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Characteristics of Ex Post Facto Studies

There may be both “treatment” and“control” groups, however these will beexisting, not assigned by the researcher

There is no manipulation of conditions

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Ex Post Facto research

Researcher cannot manipulate some variables and therefor selects participants that have certain values for those variables by themselves (gender, personality, illness, ...)

Pros

• Often only possible approach

Cons

• Selection threat to internal validity: probably not only independent variable differs among participants

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The experimenter does not manipulate the IV... that is subjects cannot be randomly

assigned to the levels of the IV - rather they assign themselves because the IV is not

manipulated, it also qualifies as a descriptive technique

When to use this?

You can use this where more powerful experimental designs are not possible; when you are unable to select, control and manipulate the factors necessary to study cause and effect relationships directly, or when control variables except a single independent variable may be unrealistic and artificial.

Ex post facto advantages and disadvantages

Advantages Show a correlation where

more rigorous experimentation is not possible

Exploratory tool Useful to avoid articiality

in the research. Shows cause and effect

relationships

Disadvantages Lack of control for

independent variable and randomizing subjects.

Never certain if causative factor has been included or identified

Relationship between two factors does not est. cause and effect.

May be regarded as too flexible.

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What is causal research?

Casual Research explores the effect of one thing on another and more specifically, the effect of one variable on another. The research is used to measure what impact a specific

change will have on existing norms and allows market researchers to predict hypothetical scenarios upon which a company can base its

business plan.

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For example, if a clothing company currently sells blue denim jeans, casual research can

measure the impact of the company changing the product design to the colour white. Following the research, company bosses will be able to decide whether changing the colour of the jeans to white

would be profitable. To summarise, casual research is a way of seeing how actions now will

affect a business in the future. 

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like "smoking causes cancer".

Causality and ex-post-facto designs.

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• Melibatkan perbandingan antara kumpulan yang sedia wujud

Contoh: Menyelidik murid-murid yang menonton rancangan belajar bahasa Inggeris melalui siaran TV dan membandingkan prestasi mereka untuk mengkaji sama ada menonton program bahasa Inggeris di TV dapat membantu meningkatkan pencapaian mata pelajaran tersebut. Murid-murid dikumpulkan dalam beberapa kumpulan mengikut banyaknya mereka menonton siaran tersebut.

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Examples of Ex Post Facto Studies

What is the effect of day care on the socialskills of children?

What is the relationship betweenparticipation in extracurricular activitiesand self concept?

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Ex Post Facto - example

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Depression in rape victims

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Research design

Y23Y22Y21 Y24X2 Y26Y25

Y13Y12Y11 Y14X1 Y16Y15

X1 = rape victim

X2 = control

Yij = average depression score in group i, at time j

---- indicates possible unequality of groups in both conditions

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Reka bentuk ini menekankan sebab yang dijangka. Contoh 1: Adakah program pendidikan pemulihan

meningkatkan prestasi 3M murid-murid yang lemah? Contoh 2: Adakah program kaunseling individu

meningkatkan motivasi murid bermasalah? Tafsiran sebab-akibat (cause-effect relationship) Penyelidik perlu memastikan:

Terdapat hubungan antara A dan B; A datang dahulu daripada B; Tidak ada kesan pemboleh ubah luaran terhadap A, B atau

interaksi A dan B.

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1. Begin with subjects who differ on an independent variable (such as their principal instrument/voice) and study how they differ on dependent variables (such as levels of performance anxiety or music theory test scores).

2. Begin with subjects who differ on a dependent variable (such as attrition from music--comparing those students who drop out of music with those who persist) and study how they differ on various independent variables (such as how much they practice, how they feel about their relationship with their teacher, how they feel about themselves as musicians, etc.).

Two Basic Approaches to Ex Post Facto Research

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Causality Example Causality

CAUSAL-COMPARATIVE RESEARCH 

"ex post facto"

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Causal-comparative research is a useful tool that can be employed in situations where

experimental designs are not possible. The researcher must remember, however, that demonstrating a relationship between two

variables (even a very strong relationship) does not "prove" that one variable actually causes the

other to change.

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1. Perhubungan statistik yang signifikan antara pemboleh ubah bebas dan bersandar wujud dengan sah.

2. Pemboleh ubah bebas wujud sebelum pemboleh ubah bersandar.

3. Pemboleh ubah lain tidak mempengaruhi pemboleh ubah bersandar.

Tiga Jenis Bukti Yang Perlu Untuk Mengesahkan Hubungan Sebab-akibat

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Empat bentuk kawalan:

1. Perubahan markah, mengambil kira markah subjek pada ujian pra dan pos.

2. Memadankan subjek mengikut ciri yang membezakan sesuatu populasi.

3. Menggunakan hanya subjek yang serupa.

4. Menggunakan analisis statistik seperti ANOVA dan ANCOVA.

Kawalan Reka Bentuk Ex Post Facto

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• Kajian perbandingan sebab perlu digunakan apabila kajian eksperimen tidak dapat dilakukan.

• Keadaan sebab mesti berlaku sebelumnya. • Pemboleh ubah ekstranous perlu

dikenalpasti dan dicatat. • Perbezaan antara kumpulan perlu dikawal.• Hubungan sebab-akibat perlu dinyatakan

dengan berhati-hati!

Tips…

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Let's say you want to determine that your new fertilizer, MegaGro, will increase the growth rate of plants. You begin by getting a plant to go with your fertilizer. Since the experiment is concerned with proving that MegaGro works, you need another

plant, using no fertilizer at all on it, to compare how much change your fertilized plant displays. This is

what is known as a control group.

Example: Causality

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Set up with a control group, which will receive no treatment, and an experimental group, which will get MegaGro, you must

then address those variables that could invalidate your experiment. This can be an extensive and exhaustive

process. You must ensure that you use the same plant; that both groups are put in the same kind of soil; that they receive equal amounts of water and sun; that they receive the same amount of exposure to carbon-dioxide-exhaling researchers, and so on. In short, any other variable that might affect the growth of those plants, other than the fertilizer, must be the same for both plants. Otherwise, you can't prove absolutely

that MegaGro is the only explanation for the increased growth of one of those plants.

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Such an experiment can be done on more than two groups. You may not only want to show that MegaGro

is an effective fertilizer, but that it is better than its competitor brand of fertilizer, Plant! All you need to do,

then, is have one experimental group receiving MegaGro, one receiving Plant! and the other (the

control group) receiving no fertilizer. Those are the only variables that can be different between the three groups; all other variables must be the same for the

experiment to be valid (see validity).

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Controlling variables allows the researcher to identify conditions that may affect the experiment's outcome. This may lead to

alternative explanations that the researcher is willing to entertain in order to isolate only variables judged significant. In

the MegaGro experiment, you may be concerned with how fertile the soil is, but not with the plants' relative position in the window, as you don't think that the amount of shade they get will affect their growth rate. But what if it did? You would have to go about eliminating variables in order to determine which is the key factor. What if one receives more shade than the other and the MegaGro plant, which received more shade, died? This

might prompt you to formulate a plausible alternative explanation, which is a way of accounting for a result that

differs from what you expected. You would then want to redo the study with equal amounts of sunlight.

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