Pretest And Posttest In Experimental Research Pdf


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pretest and posttest in experimental research pdf

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In this design, one or more independent variables are manipulated by the researcher as treatments , subjects are randomly assigned to different treatment levels random assignment , and the results of the treatments on outcomes dependent variables are observed. The unique strength of experimental research is its internal validity causality due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

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The books by Campbell and Stanley and Cook and Campbell are considered classic in the field of experimental design. The following is summary of their books with insertion of our examples. Problem and Background Experimental method and essay-writing Campbell and Stanley point out that adherence to experimentation dominated the field of education through the s Thorndike era but that this gave way to great pessimism and rejection by the late s.

However, it should be noted that a departure from experimentation to essay writing Thorndike to Gestalt Psychology occurred most often by people already adept at the experimental tradition.

Therefore we must be aware of the past so that we avoid total rejection of any method, and instead take a serious look at the effectiveness and applicability of current and past methods without making false assumptions. Replication Multiple experimentation is more typical of science than a once and for all definitive experiment! Experiments really need replication and cross-validation at various times and conditions before the results can be theoretically interpreted with confidence.

Cumulative wisdom An interesting point made is that experiments which produce opposing theories against each other probably will not have clear cut outcomes--that in fact both researchers have observed something valid which represents the truth. Adopting experimentation in education should not imply advocating a position incompatible with traditional wisdom, rather experimentation may be seen as a process of refining this wisdom. Therefore these areas, cumulative wisdom and science, need not be opposing forces.

Factors Jeopardizing Internal and External Validity Please note that validity discussed here is in the context of experimental design, not in the context of measurement. Factors which jeopardize internal validity History --the specific events which occur between the first and second measurement. Factors which jeopardize external validity Reactive or interaction effect of testing --a pretest might increase or decrease a subject's sensitivity or responsiveness to the experimental variable.

A group is introduced to a treatment or condition and then observed for changes which are attributed to the treatment X O The Problems with this design are: A total lack of control.

Also, it is of very little scientific value as securing scientific evidence to make a comparison, and recording differences or contrasts. O 1 X O 2 However, there exists threats to the validity of the above assertion: History --between O 1 and O 2 many events may have occurred apart from X to produce the differences in outcomes.

The longer the time lapse between O 1 and O 2 , the more likely history becomes a threat. X O 1 O 2 Threats to validity include: Selection --groups selected may actually be disparate prior to any treatment. An explanation of how this design controls for these threats is below. History --this is controlled in that the general history events which may have contributed to the O 1 and O 2 effects would also produce the O 3 and O 4 effects.

This is true only if the experiment is run in a specific manner--meaning that you may not test the treatment and control groups at different times and in vastly different settings as these differences may effect the results. Rather, you must test simultaneously the control and experimental groups.

Intrasession history must also be taken into consideration. For example if the groups truly are run simultaneously, then there must be different experimenters involved, and the differences between the experimenters may contribute to effects. A solution to history in this case is the randomization of experimental occasions--balanced in terms of experimenter, time of day, week and etc. The factors described so far effect internal validity.

These factors could produce changes which may be interpreted as the result of the treatment. These are called main effects which have been controlled in this design giving it internal validity. However, in this design, there are threats to external validity also called interaction effects because they involve the treatment and some other variable the interaction of which cause the threat to validity.

It is important to note here that external validity or generalizability always turns out to involve extrapolation into a realm not represented in one's sample. In contrast, internal validity are solvable within the limits of the logic of probability statistics. This means that we can control for internal validity based on probability statistics within the experiment conducted, however, external validity or generalizability can not logically occur because we can't logically extrapolate to different conditions.

Hume's truism that induction or generalization is never fully justified logically. External threats include: Interaction of testing and X --because the interaction between taking a pretest and the treatment itself may effect the results of the experimental group, it is desirable to use a design which does not use a pretest.

Research should be conducted in schools in this manner--ideas for research should originate with teachers or other school personnel. The designs for this research should be worked out with someone expert at research methodology, and the research itself carried out by those who came up with the research idea. Results should be analyzed by the expert, and then the final interpretation delivered by an intermediary.

Tests of significance for this design--although this design may be developed and conducted appropriately, statistical tests of significance are not always used appropriately. Wrong statistic in common use--many use a t-test by computing two ts, one for the pre-post difference in the experimental group and one for the pre-post difference of the control group.

If the experimental t-test is statistically significant as opposed to the control group, the treatment is said to have an effect.

However this does not take into consideration how "close" the t-test may really have been. A better procedure is to run a 2X2 ANOVA repeated measures, testing the pre-post difference as the within-subject factor , the group difference as the between-subject factor , and the interaction effect of both factors. R O 1 X O 2 R O 3 O4 R X O 5 R O 6 In this design, subjects are randomly assigned to four different groups: experimental with both pre-posttests, experimental with no pretest, control with pre-posttests, and control without pretests.

By using experimental and control groups with and without pretests, both the main effects of testing and the interaction of testing and the treatment are controlled. Therefore generalizability increases and the effect of X is replicated in four different ways. Statistical tests for this design--a good way to test the results is to rule out the pretest as a "treatment" and treat the posttest scores with a 2X2 analysis of variance design-pretested against unpretested.

And can be seen as controlling for testing as main effect and interaction, but unlike this design, it doesn't measure them. But the measurement of these effects isn't necessary to the central question of whether of not X did have an effect. This design is appropriate for times when pretests are not acceptable. Statistical tests for this design--the most simple form would be the t-test. However covariance analysis and blocking on subject variables prior grades, test scores, etc.

However, some widespread concepts may also contribute other types of threats against internal and external validity. Some researchers downplay the importance of causal inference and assert the worth of understanding. This understanding includes "what," "how," and "why. If a question "why X happens" is asked and the answer is "Y happens," does it imply that "Y causes X"? If X and Y are correlated only, it does not address the question "why. In fact, a particular explanation does not explain anything.

For example, if one askes, "Why Alex Yu behaves in that way," the asnwer could be "because he is Alex Yu. He is a unqiue human being. He has a particular family background and a specific social circle. Reference Campbell, D.

Experimental and quasi-experimental designs for research. Barbara Ohlund and Chong-ho Yu.

Pre-Experimental Designs

This article discusses the difficulties in conducting intervention research or evaluating intervention programs in a school setting. In particular, the problems associated with randomization and obtaining control groups are examined. The use of quasi-experimental designs, specifically a paired comparison design using the individual as his or her own control in combination with a Separate-Sample Pretest-Posttest Control Group Design, are suggested as one means of increasing internal and external validity while overcoming the problems associated with implementing a true experimental design. Most users should sign in with their email address. If you originally registered with a username please use that to sign in.


PDF | The pretest-posttest design referred to as Design 2 by Campbell and Stanley () is commonly Find, read and cite all the research you need on ResearchGate. magnitude of change-fer. ihe experimental group.


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We will now dive a handful of research designs in greater detail, exploring their strengths and weaknesses. Starting with a selection of experimental designs, which utilize randomization in order to allow for comparison of the intervention group s with an equivalent group s not exposed to the intervention. Randomization is the key differentiator between experimental designs and quasi-experimental or observational designs. Random assignment reduces the possibility that the exposed group s and the controls those not receiving the intervention have any variance between them that could impact the program effect.

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes. Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B. If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment.

For many true experimental designs , pretest-posttest designs are the preferred method to compare participant groups and measure the degree of change occurring as a result of treatments or interventions. Pretest-posttest designs grew from the simpler posttest only designs, and address some of the issues arising with assignment bias and the allocation of participants to groups. One example is education, where researchers want to monitor the effect of a new teaching method upon groups of children. Other areas include evaluating the effects of counseling, testing medical treatments, and measuring psychological constructs.

For many true experimental designs , pretest-posttest designs are the preferred method to compare participant groups and measure the degree of change occurring as a result of treatments or interventions. Pretest-posttest designs grew from the simpler posttest only designs, and address some of the issues arising with assignment bias and the allocation of participants to groups. One example is education, where researchers want to monitor the effect of a new teaching method upon groups of children.

Among the gold standards in human resource development HRD research are studies that test theoretically developed hypotheses and use experimental designs. A somewhat typical experimental design would involve collecting pretest and posttest data on individuals assigned to a control or experimental group. Data from such a design that considered if training made a difference in knowledge, skills or attitudes, for example, could help advance practice. Using simulated datasets, situated in the example of a scenario-planning intervention, this paper aims to show that choosing a data analysis path that does not consider the associated assumptions can misrepresent findings and resulting conclusions. A review of HRD articles in a select set of journals indicated that some researchers reporting on pretest-posttest designs with two groups were not reporting associated statistical assumptions and reported results from repeated-measures analysis of variance that are considered of minimal utility.

At the present time, sharp differences of opinion exist concerning the possible and probable consequences of giving studients a test before the commencement of instruction. There is evidence that pre-tests can have orienting and motivational and hence teaching functions — in addition to the sought-for testing function. There is also evidence that these additional functions can be either general or specific.

Pretest-Posttest Designs

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