Makalah quasi experiment and single case

PAPER
EXPERIMENTS, QUASI-EXPERIMENTS AND SINGLE-CASE RESEARCH

To fulfill one of the tasks of the Educational Quantitative Research Course which is supported by
Dr. Badawi, M.Pd

In English Education Study Program
Arranged by:
Hendriansyah
Yunida Nindiya
Zerine Muslimah





HIGH SCHOOLS AND EDUCATIONAL SCIENCES
MUHAMADIYAH KOTABUMI-LAMPUNG
JANUARY 2019




FOREWORD


  By praising and thankful to Allah SWT, who has bestowed His grace and grace upon us, so that we can finish the compilation of this paper with the title " Experiments, Quasi-Experiments And Single-Case Research".
The paper is structured in the hope of adding to our knowledge and insight.
    We realize that in writing this paper is still a lot of short comings, therefore we are looking forward to constructive criticism and suggestions. And hopefully the completion of this paper can be useful for your friends.







Kotabumi, January 2019



Author









TABLE OF CONTENTS

FOREWORD ii
TABLE OF CONTENTS iii
CHAPTER I INTRODUCTION 1
1.1 BACKGROUND 1
1.2 Problem Formulation 2
1.3 Purpose Of Writing 2
CHAPTER II 3
DISCUSSION 3
2.1 Definition 3
2.2 A ‘True’ Experimental Design: The Pretest-Post-Test Control Group Design 4
2.3 A Quasi-Experimental Design: The Nonequivalent Control Group Design 4
2.4 Single Case And Meta-Analysis 5














CHAPTER I
INTRODUCTION

BACKGROUND
The essential feature of experimental research is that investigators deliberately control and manipulate the conditions which determine the events in which they are interested. At its simplest, an experiment involves making a change in the value of one variable called the independent variable and observing the effect of that change on another variable called the dependent variable.
Imagine that we have been transported to a laboratory to investigate the properties of a new wonder-fertilizer that farmers could use on their cereal crops, let us say wheat (Morrison, 1993:44–5). The scientist would take the bag of wheat seed and randomly split it into two equal parts. One part would be grown under normal existing conditions controlled and measured amounts of soil, warmth, water and light and no other factors. This would be called the control group. The other part would be grown under the same conditions the same controlled and measured amounts of soil, warmth, water and light as the control group, but, additionally, the new wonder-fertilizer. Then, four months later, the two groups are examined and their growth measured. The control group has grown half a metre and each ear of wheat is in place but the seeds are small. The experimental group, by contrast, has grown half a metre as well but has significantly more seeds on each ear, the seeds are larger, fuller and more robust. The scientist concludes that, because both groups came into contact with nothing other than measured amounts of soil, warmth, water and light, then it could not have been anything else but the new wonder-fertilizer that caused the experimental group to flourish so well. The key factors in the experiment were:
• the random allocation of the whole bag of wheat into two matched groups (the control and the experimental group), involving the initial measurement of the size of the wheat to ensure that it was the same for both groups (i.e. the pretest);
 • the identification of key variables (soil, warmth, water, and light);
• the control of the key variables (the same amounts to each group);
• the exclusion of any other variables;
• the giving of the special treatment (the intervention) to the experimental group whilst holding every other variable constant for the two groups;
• the final measurement of yield and growth (the post-test);
• the comparison of one group with another;
 • the stage of generalization—that this new wonder-fertilizer improves yield and growth under a given set of conditions.
Frequently in learning experiments in classroom settings the independent variable is a stimulus of some kind, a new method in arithmetical computation for example, and the dependent variable is a response, the time taken to do twenty problems using the new method. Most empirical studies in educational settings, however, are quasi-experimental rather than experimental. The single most important difference between the quasi-experiment and the true experiment is that in the former case, the researcher undertakes his study with groups that are intact, that is to say, the groups have been constituted by means other than random selection. We begin by identifying the essential features of pre-experimental, true experimental and quasi-experimental designs, our intention being to introduce the reader to the meaning and purpose of control in educational experimentation.

Problem Formulation
Based on the above background we formulate several problems that are:
What is experiment?
 What is true experiment?
What  is quasi experiment?
What are single case and meta-analysis?
Purpose Of Writing

To know definition and example experiment
To know true experiments
To know quasi experiments
To know single case and meta-analysis

CHAPTER II
DISCUSSION

Definition
Experiment is a set of actions and observations, carried out to check or blame the hypothesis or recognize a causal relationship between symptoms. An important feature of experimental research is that researchers intentionally control and manipulate conditions that determine the events in which they are interested. In the outline of research designs that follows we use symbols and conventions from Campbell and Stanley (1963):
1 χ represents the exposure of a group to an experimental variable or event, the effects of which are to be measured.
2 O refers to the process of observation or measurement.
 3 χs and Os in a given row are applied to the same persons.
 4 Left to right order indicates temporal sequence.
5  χs and Os vertical to one another are simultaneous.
6 R indicates random assignment to separate treatment groups.
7 Parallel rows unseparated by dashes represent comparison groups equated by randomization, while those separated by a dashed line represent groups not equated by random assignment.
A Pre-Experimental Design: The One Group Pretest-Post-Test
This design is useful for getting initial information about the questions in the study. On this design are pretest before being treated. Thus the results of the treatment can be known more accurately, because it can compare with the situation before being treated. In this model there is one experimental group then given a pretest to determine the initial state of the experimental group, then given treatment and given the posttest. This design can be described as follows:


O1 = the value of pretest (prior to treated the use of multimedia)
O2 = value posttest (After being treated the use of multimedia)
X = Variable use of multimedia instructional treatment.

A ‘True’ Experimental Design: The Pretest-Post-Test Control Group Design
Real experimental design is the design of the pretest-post-test control group in this design the researcher can control all external variables that influence the course of the experiment. Thus internal validity (the quality of the implementation of the research design) can be high. The main characteristic of true experimental is that, the sample used as an experiment or as a control group was taken randomly from a certain population. So the characteristic is the presence of a control group and randomly selected samples.
Pretest-Posttest Control Group Design.
In this design there are two groups randomly selected, then given a pretest to find out the initial state is there a difference between the experimental group and the control group.


A Quasi-Experimental Design: The Nonequivalent Control Group Design
Often in educational research, it is simply not possible for investigators to undertake true experiments. At best, they may be able to employ something approaching a true experimental design in which they have control over what Campbell and Stanley (1963) refer to as ‘the who and to whom of measurement’ but lack control over ‘the when and to whom of exposure’, or the randomization of exposures—essential if true experimentation is to take place. These situations are quasi-experimental and the methodologies employed by researchers are termed quasi-experimental designs. (Kerlinger (1970) refers to quasi-experimental situations as ‘compromise designs’, an apt description when applied to much educational research where the random selection or random assignment of schools and classrooms is quite impracticable.) One of the most commonly used quasi-experimental designs in educational research can be represented as:
E O1 X1 O2
-----------------
C O3 X2 O4
The dashed line separating the parallel rows in the diagram of the non-equivalent control group indicates that the experimental and control groups have not been equated by randomization hence the term ‘non-equivalent’. The addition of a control group makes the present design a decided improvement over the one group pretest-post-test design, for to the degree that experimenters can make E and C groups as equivalent as possible, they can avoid the equivocality of interpretations that plague the pre experimental design discussed earlier. The equivalence of groups can be strengthened by matching, followed by random assignment to E and C treatments. Where matching is not possible, the researcher is advised to use samples from the same population or samples that are as alike as possible (Kerlinger, 1970). Where intact groups differ substantially, however, matching is unsatisfactory due to regression effects which lead to different group means on post-test measures. Campbell and Stanley put it this way:
  If [in the non-equivalent control group design] the means of the groups are substantially different, then the process of matching not only fails to provide the intended equation but in addition insures the occurrence of unwanted regression effects. It becomes predictably certain that the two groups will differ on their post-test scores altogether independently of any effects of , and that this difference will vary directly with the difference between the total populations from which the selection was made and inversely with the test-retest correlation.

Single Case And Meta-Analysis
Single-case research: ABAB design
Single-case research as an experimental methodology has extended to such diverse fields as clinical psychology, medicine, education, social work, psychiatry, and counselling. Most of the single-case studies carried out in these (and other) areas share the following characteristics:
• they involve the continuous assessment of some aspect of human behaviour over a period of time, requiring on the part of the researcher the administration of measures on multiple occasions within separate phases of a study.
• they involve ‘intervention effects’ which are replicated in the same subject(s) over time.
The characteristics of single-case research studies are discussed by Kazdin (1982) in terms of ABAB designs, the basic experimental format in most single-case researches. ABAB designs, Kazdin observes, consist of a family of procedures in which observations of performance are made over time for a given client or group of clients. Over the course of the investigation, changes are made in the experimental conditions to which the client is exposed.
An example of the application of the ABAB design in an educational setting is provided by Dietz (1977)5 whose single-case study sought to measure the effect that a teacher could have upon the disruptive behaviour of an adolescent boy whose persistent talking disturbed his fellow classmates in a special education class.
Meta-Analysis In Educational Research
The study by Bhadwal and Panda (1991) is typical of research undertaken to explore the effectiveness of classroom methods. Often as not, such studies fail to reach the light of day, particularly when they form part of the research requirements for a higher degree. Meta-analysis is, simply, the analysis of other analyses. It involves aggregating the results of other studies into a coherent account.
The term ‘meta-analysis’ originated in 1976 (Glass, 1976) and early forms of meta-analysis used calculations of combined probabilities and frequencies with which results fell into defined categories (e.g. statistically significant at given levels), though problems of different sample sizes confounded rigour (e.g. large samples would yield significance in trivial effects, whilst important data from small samples would not be discovered because they failed to reach statistical significance) (Light and Smith, 1971; Glass et al., 1981; McGaw, 1997:371). Glass (1976) and Glass et al. (1981) suggested three levels of analysis: (a) primary analysis of the data; (b) secondary analysis, a re-analysis using different statistics; (c) meta-analysis analysing results of several studies statistically in order to integrate the findings. Glass et al. (1981) and Hunter et al. (1982) suggest several stages in the procedure:
Step 1 Identify the variables for focus (independent and dependent).
Step 2 Identify all the studies which feature the variables in which the researcher is interested.
Step 3 Code each study for those characteristics that might be predictors of outcomes and effect sizes. (e.g. age of participants, gender, ethnicity, duration of the intervention).
Step 4 Estimate the effect sizes through calculation for each pair of variables (dependent and independent variable) (see Glass, 1977), weighting the effect size by the sample size.
Step 5 Calculate the mean and the standard deviation of effect sizes across the studies, i.e. the variance across the studies.
Step 6 Determine the effects of sampling errors, measurement errors and range of restriction.
Step 7 If a large proportion of the variance is attributable to the issues in Step 6, then the average effect size can be considered an accurate estimate of relationships between variables.
Step 8 If a large proportion of the variance is not attributable to the issues in Step 6, then review those characteristics of interest which correlate with the study effects.
Wood (1995:393) suggests that effect-size can be calculated by dividing the significance level by the sample size. Glass et al. (1981:29, 102) calculate the effect size as:

Further, Wood (1995:296) suggests that metaanalysis oversimplifies results by concentrating on overall effects to the neglect of the interaction of intervening variables. To the charge that, because meta-analyses are frequently conducted on large data sets where multiple results derive from the same study (i.e. that the data are non-independent) and are therefore unreliable, Glass et al. (1981) indicate how this can be addressed by using sophisticated data analysis techniques (pp. 153–216). Finally, a practical concern is the time required not only to use the easily discoverable studies (typically large-scale published studies) but to include the smaller-scale unpublished studies; the effect of neglecting the latter might be to build in bias in the meta-analysis.