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Chapter 10

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Chapter X: Non-Experimental & Quasi-Experimental Strategies Non-Equivalent Group, Pre-Post, and Developmental Designs 10.1 Non-Experimental & Quasi-Experimental Research Strategies • Five basic research strategies: experimental, non-experimental, quasi-experimental, correlational, and descriptive. • A researcher can often devise a research strategy (a method of collecting data) that is similar to an experiment but fails to satisfy at least one of the requirements of a true experiment. • Such studies are generally called non-experimental or quasi-experimental research studies. • Although these studies resemble experiments, they always contain a confounding variable or other threat to internal validity that is an integral part of the design and simply cannot be removed. • The existence of a confounding variable means that these studies cannot establish unambiguous cause-and-effect relationships and, therefore, are not true experiments. • The distinction between the non-experimental research strategy and the quasi- experimental research strategy is the degree to which the research strategy limits confounding and controls threats to internal validity. • If a research design makes little or no attempt to minimize threats, it is classified as non- experimental. A quasi-experimental design, on the other hand, makes some attempt to minimize threats to internal validity and approaches the rigor of a true experiment. • A quasi-experimental design, on the other hand, makes some attempt to minimize threats to internal validity and approaches the rigor of a true experiment. • The fact that quasi-experimental and non-experimental studies are not true experiments does not mean that they are useless or even second-class research studies. • Non-experimental and quasi-experimental studies often look like experiments in terms of the general structure of the research study. • A non-experimental or quasi-experimental study typically involves comparing groups of scores. • One variable is used to create groups or conditions, then a second variable is measured to obtain a set of scores within each condition. • The different groups or conditions are not created by manipulating an independent variable. • Groups are usually defined in terms of a pre-existing participant variable or in terms of time. • Two methods of defining groups produce two general categories of non-experimental and quasi-experimental designs: 1. Between-subjects designs, also known as non-equivalent group designs. 2. Within-subjects designs, also known as pre-post designs. The non-experimental research strategy makes little or non attempt to control threats to internal validity whereas the quasi-experimental research strategy attempts to limit threats to internal validity. Both strategies, like true experiments, typically involve a comparison of groups or conditions. However, these two strategies use a non-manipulated variable to define the groups or conditions being compared. The non-manipulated variable is usually a participant variable (such as male versus female) or a time variable (such as before versus after treatment). 10.2 Between-Subjects Non-Experimental & Quasi-Experimental Designs: Non- Equivalent Group Designs • Between-subjects experimental design refers to a method of comparing two or more treatment conditions using a different group of participants in each condition. • A common element to between-subjects experiments is the control of participant variables by assigning participants to specific treatment conditions. • The goal is to balance or equalize participant variables across treatment conditions by using a random process or by deliberately matching participants. • Note that the researcher attempts to create equivalent groups of participants by actively deciding which individuals go into which groups. • When the researcher cannot use random assignment or matching to balance participant variables across groups, there is no assurance that the two groups are equivalent. In this situation, the research study is called a non-equivalent group design. A non-equivalent group design is a research study in which the different groups of participants are formed under circumstances that do not permit the researcher to control the assignment of individuals to groups, and the groups of participants are, therefore, considered non-equivalent. Specifically, the researcher cannot use random assignment to create groups of participants. • A non-equivalent group design has a built-in threat to internal validity that precludes an unambiguous cause-and-effect explanation: assignment bias. • Assignment bias occurs whenever the assignment procedure produces groups that have different participant characteristics • In a non-equivalent group design, there is no random assignment and there is no assurance of equivalent groups. • Three common examples of non-equivalent group designs: (1) the differential research design, (2) the post-test-only non-equivalent control group design, and (3) the pretest- posttest non-equivalent control group design. • Non-equivalent group research involves no manipulation but simply attempts to compare pre-existing groups that are defined by a particular participant variable • A research study that simply compares pre-existing groups is called a differential research design because its goal is to establish differences between the pre-existing groups. • This type of study is often called ex post facto research because it looks at differences “after the fact;” that is, at differences that already exist between groups. • Differential research design makes no attempt to control the threat of assignment bias; it is classified as a non-experimental research design. A research study that simply compares pre-existing groups is called a differential research design. A differential study uses a participant characteristic such as gender, race, or personality to automatically assign participants to groups. The researcher does not randomly assign individuals to groups. A dependent variable is then measured for each participant to obtain a set of scores within each group. The goal of the study is to determine whether the scores for one group are consistently different from the scores of another group. Differential research is classified as a non-experimental research design. • Many researchers place differential research in the same category as correlational research based on their similarities. • In differential and correlational studies, a researcher simply observes two naturally occurring variables without any interference or manipulation. • The subtle distinction between differential research and correlational research is whether or not one of the variables is used to establish separate groups to be compared. • In differential research, participant differences in one variable are used to create separate groups, and measurements of the second variable are made within each group. • The researcher then compares the measurements for one group with the measurements of another group, typically looking at mean differences between groups. • A correlational study treats all participants as a single group and simply measures the two variables for each individual. • Both designs allow researchers to establish the existence of relationships and to describe relationships between variables, but neither design permits a cause-and-effect explanation of the relationship. Posttest-Only Non-Equivalent Control Group Design • Non-equivalent groups are commonly used in applied research situations in which the goal is to evaluate the effectiveness of a treatment administered to a pre-existing group of participants. • A second group of similar but non-equivalent participants is used for the control condition. • The researcher uses pre-existing groups and does not control the assignment of participants to groups. • The researcher does not randomly assign individuals to groups. A non-equivalent control group design uses pre-existing groups, one of which serves in the treatment condition and the other in the control condition. The researcher does not randomly assign individuals to groups. • One common example of a non-equivalent control group design is called a posttest-only non-equivalent control group design. • This type of study is also called a static group comparison. • In this type of design, one group of participants is given a treatment and then is measured after the treatment, • The scores for the treated group are then compared with the scores from a non- equivalent group that has not received the treatment. A posttest-only non-equivalent control group design, also known as a static group comparison, compares two non-equivalent groups of participants. One group is observed (measured) after receiving a treatment, and the other group is measured at the same time but receives no treatment. This is an example of a non-experimental research design. • The posttest-only non-equivalent control group design does not address the threat of assignment bias; it is considered a non-experimental design. Pretest-Only Non-Equivalent Control Group Design • A much stronger version of the non-equivalent control group design is often called a pretest-posttest non-equivalent control group design. • Both groups are observed (measured) prior to the treatment. • The treatment is then administered to one group, and following the treatment, both groups are observed again. • The addition of the pretest measurement allows researchers to address the problem of assignment bias that exists with all non-equivalent groups’ research. • The researcher then compares the observations before treatment to establish whether or not the two groups really are similar. • This type of design allows a researcher to compare the pretest scores and posttest scores for both groups to determine whether the treatment or some other, time related factor is responsible for changes. • In the pre-test-posttest non-equivalent groups design, time-related threats are minimized because both groups are observed over the same time period and, therefore, should experience the same time-related factors. • The pre-test-posttest non-equivalent control group design reduces the threat of assignment bias, limits threats from time-related factors, and can provide some evidence to support a cause-and-effect relationship. • As a result, this type of research is considered quasi-experimental. A pre-test-posttest non-equivalent design compares two non-equivalent groups. One group is measured twice, once before a treatment is administered and once after. The other group is measured at the same two times but does not receive any treatment. Because this design to limit threats to internal validity, it is classified as quasi-experimental. • The addition of a pre-test to the non-equivalent control group design reduces some threats to internal validity; it does not eliminate them completely. • The fact that the groups are non-equivalent and often separated creates the potential for other threats. • It is possible for a time-related threat to affect the groups differently. • The history for one group could be different from that for the other group. • History effects that differ form one group to another are called differential history effects. • In a non-equivalent group design, there is always the possibility that differential history (and not the treatment) is causing the groups to be different. • The different groups are not selected from the same source; they may be exposed to different environmental factors that can influence their scores. • The internal validity of a pre-test-posttest non-equivalent control group design may be threatened by differential instrumentation, differential testing effects, differential maturation, or differential regression. 10.3 Within-Subjects Non-experimental & Quasi-Experimental Designs: Pre-Post Designs • In a typical pre-post study, one group of participants is observed (measured) before and after a treatment or event. • The goal of the pre-post design is to evaluate the influence of the intervening treatment or event by comparing the observations made before treatment with the observations made after treatment. • Whenever a research study involves repeated observations over time, time-related factors can threaten internal validity. • History, instrumentation, testing effects, maturation, and statistical regression are five kinds of time-related threats. • Any differences found between the pre-treatment observations and the post-treatment observations could be explained by history, instrumentation, testing effects, maturation, or regression. • In a pre-post design, it is impossible to counterbalance the order of treatments. • Specifically, the before-treatment observations (Pretest) must always precede the after- treatment observations (posttest). • The internal validity of a pre-post study is threatened by a variety of factors related to the passage of time. • During the time between the first observation and the last observation, any one of these factors could influence the participants and cause a change in their scores. • Unless these factors are controlled or minimized by the structure of the research design, a pre-post study cannot approach the internal validity of a true experiment. One Group Pretest-Posttest Design • The simplest version of the pre-post design consists of only one observation for each participant made before the treatment or event, and only one observation made after it. In the one-group pretest-posttest design, each individual in a single group of participants is measured once before treatment and once after treatment. This type of research is classified as a non-experimental design. Time-Series & Interrupted Time-Series Designs • A time-series design requires a series of observations for each participant before and after the treatment or event. • A time-series design requires a series of observations for each participant before and after the treatment or event. • The intervening treatment may or may not be manipulated by the researcher. • When the event that occurs in the middle of the series of observations is actually a treatment manipulated or administered by the researcher, the research study is called a time-series design. • A study in which the intervening event is not manipulated by the researcher is called an interrupted time-series design. A time-series design has a series of observations for each participant before a treatment and a series of observations after the treatment. The treatment is administered by the researcher. An interrupted time-series design consists of a series of observations for each participant before an event occurs and after the event occurs. The event is not a treatment or an experience that is created or manipulated by the researcher. • For both time-series design, the pretest and posttest series of observations serve several valuable purposes. • Pretest observations allow a researcher to see any trends that may already exist in the data before the treatment is even introduced. • Trends in the data are an indication that the scores are influenced by some factor unrelated to the treatment. • If the data show no trends or major fluctuations before the treatment, the researcher can be reasonably sure that potential threats to internal validity are not influencing the participants. • The series of observations allows a researcher to minimize most threats to internal validity. As a result, the time-series and interrupted time-series designs are classified as quasi-experimental. • Time-series and interrupted time-series designs minimize most threats to internal validity. • However, it is possible for some outside event – history – to influence the data. • If the outside event occurs at any time other than the introduction of the treatment, it should be easy to separate the history effects from the treatment effects. • History effects (outside events) are a threat to validity only when there is a perfect correspondence between the occurrence of the event and the introduction of the treatment. • The series of observations after the treatment or event also allows a researcher to observe any post-treatment trends. Equivalent Time-Samples Design • One way of minimizing the threat of coincidence in a time series design is to expand the simple time series into an equivalent time-samples design. • In an equivalent time-samples study, the treatment is repeatedly administered and removed during the series of observations. • The study begins with a series of observations, which is followed by a treatment and another series of observations. • Then the treatment is removed for a series of no-treatment observations. • This sequence is repeated with at least one more set of treatment observations and no- treatm
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