X 1 O X2 O Desain ini mengatasi kelemahan desain sebelumnya karena dapat mengetahui efek interaksi pretest dengan perlakuan secara langsung dan kesulitan dalam pelaksanaannya dalam situasi praktis, lebih banyak waktu dan usaha yang diperlukan dua percobaan secara bersamaan serta masalah pada peningkatan jumlah subjek yang sama yang akan diperlukan untuk empat kelompok. Analisis data dapat diuji dengan uji ragam multi jalur pola kovarians, dimana pretest dijadikan kovariabel, termasuk bila desainnya diperluas, dikombinasi dengan uji t untuk melihat efek interaksi pretest dan perlakuan.
Bila tidak memenuhi persyaratan parametrik maka diuji menggunakan kombinasi uji peringkat bertanda Wilcoxon dan uji U Mann-whitney. Penelitian faktorial desain adalah modifikasi dari true experiment, yaitu memungkinkan adanya variabel moderator yang mempengaruhi treatment terhadap hasil.
Penelitian ini harus terdapat pengukuran sebelum treatment, jika masuk ke dalam true eksperiment. Penelitian ini disebut juga penelitian bifaktor karena melibatkan lebih dari satu variabel bebas yang dijadikan faktor. Dan kedua faktor tersebut secara teoritik teradapat interaksi. Penelitian ini diketahui terbagi dalam dua jenis, yaitu ekperimen bifaktorial yang merupakan melibatkan dua faktor, sedangkan eksperimen faktorial trifaktor yang melibatkan tiga faktor.
Beberapa contoh faktor yang dapat muncul dan mempengaruhi penelitian eksperimen sesungguhnya adalah:. Historis mungacu pada munculnya suatu kejadian yang bukan dari perlakuan eksperimen, tetapi dapat mempengaruhi performansi pada variabel bebas. Sesuatu yang agak lama, faktor historis mungkin menjadi suatu masalah. Sebagai contoh faktor historis adalah latar belakang atau pengalaman belajar pada jenjang pendidikan sebelumnya. Maturasi mengacu pada perubahan fisik atau mental pada diri subyek selama suatu periode waktu.
Perubahan ini dapat mempengaruhi performansi subyek pada pengukuran variabel terikat. Khususnya dalam studi yang diselesaikan dalam waktu yang panjang, subyek dapat menjadi sebagai contoh lebih terkoordinasi, lebih termotivasi, tidak termotivasi atau bosan. Perubahan-perubahan tersebut dapat mengakibatkan bias pada hasil pengukuran. Regresi statistik biasanya muncul bila subyek yang dipilih berdasarkan skor ekstrem dan mengacu pada kecenderungan subyek yang memiliki skor yang paling tinggi pada pre-test ke skor yang lebih rendah pada post-test, dan subyek yang memiliki skor paling rendah pada pre-test ke skor yang lebih tinggi pada post-test.
Kecenderungannya adalah skor bergerak mundur regresi atau bergerak kea rah rata-rata mean atau skor yang diharapkan. Interaksi pre-test muncul bila respons subyek atau mengalami reaksi berbeda pada perlakuan karena mereka mengikuti pre-test. Suatu pre-test mungkin membuat peka atau mengingatkan subyek pada hakikat perlakuan. Oleh karena itu, hal ini diupayakan untuk dikontrol atau dikendalikan pada penelitian eksperimen sesungguhnya karena juga menguji kelompok yang tidak menggunakan pre-test, baik pada kelopmpok eksperimen maupun kelompok pembanding.
Dengan demikian, faktor-faktor tersebut perlu dikontrol atau dikendalikan. Sehingga pengaruh variabel bebas terhadap variabel terikat dapat menunjukkan hubungan sebab-akibat tanpa ada pengaruh dari variabel lain. Suatu penelitian, termasuk eksperimen, perlu menetapkan target populasi. Untuk penelitian eksperimen dibutuhkan keadaan populasi yang relatif homogen. Homogenitas populasi ini berguna bagi kemudahan dalam pengambilan sampel dan perlakuan yang hendak diberikan.
Jika upaya homogenitas ini dicapai secara maksimal, maka sangat membantu peningkatan validitas penelitian. Homogenitas dalam hal dapat dipahami misalnya seperti, seluruh siswa populasi berasal dari sekolah yang sama, tingkat satuan pendidikan yang sama, jenjang kelas yang sama, konsentrasi keilmuan jurusan yang sama.
Sebagaimana yang telah dijelaskan, teknik pengambilan sampel pada penelitian ini adalah random sampling. Teknik random sampling merupakan teknik pengambilan sampel yang memungkinkan seluruh anggota populasi terpilih menjadi sampel dalam penelitian.
Pada umumnya teknik random sampling yang biasa digunakan adalah Simple Random Sampling random sederhana , yang merupakan pengambilan sampel dari populasi yang dilakukan secara acak tanpa memperhatikan strata yang ada dalam populasi tersebut. Dalam penelitian kuantitatif, analisa data merupakan kegiatan setelah data dari seluruh responden atau sumber data lain terkumpul. Kegiatan dalam analisis data adalah: mengelompokkan data berdasarkan variabel dan jenis responden, mentabulasi daya berdasarkan variabel dari seluruh responden, menyajikkan data tiap variabel yang diteliti, melakukan perhitungan untuk menjawab rumusan masalah, dan melakukan perhitungan untuk menguji hipotesis yang telah diajukan.
Teknik analisa data yang digunakan dalam penelitian eksperimen sesungguhnya, yaitu statistik deskriptif dan statistik inferensial. Analisis data secara deskriptif dilakukan dengan menyajikan, mendeskripsikan, serta mengkomunikasikan data mentah menjadi bentuk tabel, grafik atau gambar.
Introduction …………………………………………………………………………………………………………………… Research Method ……………………………………………………………………………………………………………….. Explain the difference between true experimental design and quasi-experimental research design. Provide examples. Quasi-experimental research designs and experimental research designs both have one aim, which is to test a casual hypothesis UNICEF, True experimental designs are preferred by most researchers as it weighs higher on the internal validity scale, but in some cases it is impossible to randomise participants because of some characteristics such as a gender or marital status.
True experimental designs are used in various scientific experiments including drug trials, where participants all have the same disease or illness, but a portion of participants receive the treatment and a portion receive placebos.
Quasi experimental designs are used in studies such as comparing the achievement of the first born children with that of the later Conley, Pfeiffer, Velez, What does it mean to randomly assign participants to groups and why does it.
Get Access. Read More. The Scientific Method Of Experimental Testing Words 6 Pages 1 Scientific Method The scientific method can be defined as a technique for research where the problem is known, appropriate data is gathered, a hypothesis is formulated from the data, and the hypothesis is tested firsthand. The only stipulation is that the subjects must be randomly assigned to groups, in a true experimental design, to properly isolate and nullify any nuisance or confounding variables.
Of those discussed, this method is the most effective in terms of demonstrating cause and effect but it is also the most difficult to perform.
The pretest posttest equivalent groups design provides for both a control group and a measure of change but also adds a pretest to assess any differences between the groups prior to the study taking place.
To apply this design to our work experience study, we would select students from the college at random and then place the chosen students into one of two groups using random assignment.
The treatment, or work experience would be applied to one group and a control would be applied to the other. Posttest Equivalent Groups Study. Pretest-posttest designs are an expansion of the posttest only design with nonequivalent groups, one of the simplest methods of testing the effectiveness of an intervention.
In this design, which uses two groups, one group is given the treatment and the results are gathered at the end. The control group receives no treatment, over the same period of time, but undergoes exactly the same tests. Statistical analysis can then determine if the intervention had a significant effect.
One common example of this is in medicine; one group is given a medicine, whereas the control group is given none, and this allows the researchers to determine if the drug really works. This type of design, whilst commonly using two groups, can be slightly more complex. For example, if different dosages of a medicine are tested, the design can be based around multiple groups. Whilst this posttest only design does find many uses, it is limited in scope and contains many threats to validity.
Even with randomization of the initial groups, this failure to address assignment bias means that the statistical power is weak. The results of such a study will always be limited in scope and, resources permitting; most researchers use a more robust design, of which pretest-posttest designs are one.
The posttest only design with non-equivalent groups is usually reserved for experiments performed after the fact, such as a medical researcher wishing to observe the effect of a medicine that has already been administered. Randomization and the comparison of both a control and an experimental group are utilized in this type of study.
Each group, chosen and assigned at random is presented with either the treatment or some type of control. Posttests are then given to each subject to determine if a difference between the two groups exists.
While this is approaching the best method, it falls short in its lack of a pretest measure. It is difficult to determine if the difference apparent at the end of the study is an actual change from the possible difference at the beginning of the study. In other words, randomization does well to mix subjects but it does not completely assure us that this mix is truly creating an equivalency between the two groups.
The Two Group Control Group Design This is, by far, the simplest and most common of the pretest-posttest designs, and is a useful way of ensuring that an experiment has a strong level of internal validity. The principle behind this design is relatively simple, and involves randomly assigning subjects between two groups, a test group and a control.
Both groups are pre-tested, and both are post-tested, the ultimate difference being that one group was administered the treatment.
This test allows a number of distinct analyses, giving researchers the tools to filter out experimental noise and confounding variables. The internal validity of this design is strong, because the pretest ensures that the groups are equivalent.
The various analyses that can be performed upon a two-group control group pretest-posttest designs are Fig 1 :. This design allows researchers to compare the final posttest results between the two groups, giving them an idea of the overall effectiveness of the intervention or treatment.
The researcher can see how both groups changed from pretest to posttest, whether one, both or neither improved over time. If the control group also showed a significant improvement, then the researcher must attempt to uncover the reasons behind this.
A and A1 3. The researchers can compare the scores in the two pretest groups, to ensure that the randomization process was effective. B These checks evaluate the efficiency of the randomization process and also determine whether the group given the treatment showed a significant difference.
To establish causality, one must use an experimental or quasi-experimental design. Note that it is never possible to prove causality, but only to show to what degree it is probable.
Random assignment is not always possible or practical, however. This means that the program's impact is still unknown because scores might differ due to some other differences between the groups besides those that might be attributed to the program.
If issues are not pressing, using a wait-list control group is an acceptable and often-used solution. Problems with Pretest-Posttest Designs The main problem with this design is that it improves internal validity but sacrifices external validity to do so. There is no way of judging whether the process of pre-testing actually influenced the results because there is no baseline measurement against groups that remained completely untreated.
For example, children given an educational pretest may be inspired to try a little harder in their lessons, and both groups would outperform children not given a pretest, so it becomes difficult to generalize the results to encompass all children.
The other major problem, which afflicts many sociological and educational research programs, is that it is impossible and unethical to isolate all of the participants completely.
If two groups of children attend the same school, it is reasonable to assume that they mix outside of lessons and share ideas, potentially contaminating the results.
On the other hand, if the children are drawn from different schools to prevent this, the chance of selection bias arises, because randomization is not possible. The two-group control group design is an exceptionally useful research method, as long as its limitations are fully understood. For extensive and particularly important research, many researchers use the Solomon four-group method, a design that is more costly, but avoids many weaknesses of the simple pretest-posttest designs. Advantages and Disadvantages Martyn Shuttleworth in his article in Explorable.
Firstly, they can be almost too perfect, with the conditions being under complete control and not being representative of real world conditions. They can also be very impractical.
Whilst they can be cumbersome and expensive to set up, literature reviews, qualitative research and descriptive research can serve as a good precursor to generate a testable hypothesis, saving time and money.
Whilst they can be a little artificial and restrictive, they are the only type of research that is accepted by all disciplines as statistically provable.
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