Basic Between-Subjects Design PDF

Basic Between-Subjects Design Group 2 ICE BREAKER “Bawal Judgmental” The researcher decides on the experimental design mainly on the basis of three factors; the number of independent variables in the hypothesis. the number of treatment conditions needed to make a fair test of...

Basic Between-Subjects Design Group 2 ICE BREAKER “Bawal Judgmental” The researcher decides on the experimental design mainly on the basis of three factors; the number of independent variables in the hypothesis. the number of treatment conditions needed to make a fair test of the hypothesis. whether the same or different subjects are used in each of the treatment conditions. Subjects In studies with a between-subjects design, the various conditions or treatments are independent variables, and the measured effects are dependent variables. When two or more groups are used in a study, the information acquired is referred to as between-group data. Variables Using a between-subjects design. In a between-subjects design, there is usually at least one control group and one experimental group, or multiple groups that differ on a variable (e.g., gender identity, ethnicity, test score, etc.) Practical limits Disadvantages: Needs larger samples for high power. Uses more resources to recruit participants, administer sessions, cover costs, etc. Individual differences may be an alternative explanation for results. One Independent Variable refers to a research design in which only a single factor is manipulated or changed to observe its effect on a dependent variable. Two-group design There are two variations of the two-group design: One is the two-independent- groups design; the other is two-matched-groups design. Both use two treatment conditions, but they differ dramatically in how the researcher decides which subjects will take part in each treatment condition. Two-Independent Groups a type of experimental design in which two separate groups of participants are used to assess the effects of an independent variable. Two-independent-groups design a subject are placed in each of two treatment conditions through random assignment. Random Assignment - means that every subject has an equal chance being place in any of the treatment conditions. Experimental Group-Control Group Design - to asses the impact of the independent variable , we must have at least two different treatment conditions so that we can compare th effect of different values of the independent variable. Experimental Condition - we apply a particular value of our independent variable to the subjects and measure the dependent variable. Experimental Group - the subjects in an experimental condition. Control Condition - use to determine the value of the dependent variable without an experimental manipulation of the independent variable. Control Group - subjects in a control condition. Two Experimental Groups Design This is a type of between-subjects design where participants are randomly assigned to one of two independent groups, each receiving different treatments or conditions. Forming Two Independent Groups Creating two independent groups involves random assignment to ensure there is no systematic bias between the groups. Two-Independent-Groups Design A two-independent-groups design is used in experiments when we want to compare two separate groups under different conditions. Example: We're studying whether cockroaches run faster with or without an audience, we would create two groups: one group of cockroaches running with an audience and another running without one. By randomly assigning cockroaches to each group, we assume that any natural differences (like size or weight) are evenly distributed between groups. This way, if there is a difference in running speed, we can attribute it to the presence or absence of an audience. Two matched group design is used when randomization alone doesn’t ensure similar groups. In this design, participants are matched on specific traits that could affect the outcome, ensuring both groups are comparable. This reduces confounding variables, making it easier to attribute differences to the treatment itself. Matching Before and After an Experiment To form matched groups, subjects must be measured on the extrancous variable that will be used for the matching. Table 9.3 shows the way our cockroaches might be divided into two groups matched on weight. Once the roaches have been weighed, we separate them into pairs. The members of each pair are selected so that they have similar weights. For instance, the first pair is made up of subjects 2 and 10. Subject 2 weighs 1.26 grams; subject 10 weighs 1.18 grams. Although members of each pair are not exactly equal in weight, they are closer to each other than to any other cockroaches in the sample. When it is not possible to form pairs of subjects that are identical on the matching variable, the researcher must decide how much of a discrepancy will be tolerated. A difference of 0.8 grams might be acceptable, but a difference of 2 grams might not. We obviously want to make good enough matches to ensure that our groups are not significantly different on the matching variable. If there is no suitable match for an individual in the sam-pic, that individual must be eliminated from the study. After all the pairs have been formed, we randomly assign one member of each pair to a treatment condition; then, the remaining member is placed in the other condition. We can do this simply by flipping a coin. It is very important to put the members of each pair into the treatment conditions at random. If we do not, we could create a new source of confounding-exactly what we are trying to avoid. Precision matching in which we insist that the members of the matched pairs have identical scores. Range matching A more common procedure in which we require that the members of a pair fall within a previously specified range of scores. Rank-ordered matching The subjects are simply rank ordered by their scores on the matching vari-able, and subjects with adjacent scores then become a matched pair. With rank-ordered matching, we do not specify an acceptable range between members of each pair. When to Use Two Matched Groups The advantages by matching on a variable that is likely to have a strong effect on the dependent variable, we can eliminate one possible source of confounding. We do not need to assume that our treatment groups are comparable on an important extrancous variable; we can make them comparable through matching. Multiple Groups Multiple-groups design A between-subjects design with one independent variable, in which there are more than two treatment conditions. Multiple-independent-groups design The most commonly used multiple-groups design in which the subjects are assigned to the different treatment conditions at random. Multiple Groups Assigning Subjects When there are only two treatment conditions, assigning subjects randomly to each condition is a simple matter. But how do we deal with the problem of assigning subjects to several treatment condition in a such way that no bias result? Multiple Groups Assigning Subjects Block randomization a process of randomization that first creates treatment blocks containing one random order of the conditions in the experiment; subjects are then assigned to fill each successive treatment block. Multiple Groups Multiple Groups Choosing Treatment What if we decide that we need more than two treatment conditions? How do we know how many to use? Some variables have an infinite number of possible values; we could not test them all even if we wanted to. We might be able to create many different conditions, but using all of them might not make sense. Practical Limits As you set up experiments, you will make decisions about which comparisons will provide the most appropriate test of the hypothesis. An experiment that includes several levels of the independent variable can often yield more information than one that includes only two groups. However, practical considerations also affect choice of design. The multiple-groups procedure assumes that treatment groups are formed by random assignment. Thus, there will be as many different treatment groups in the experiment as there are levels of the independent variable. A pilot study is like a mini-experiment in which treatments are tested on a few subjects to see whether the levels seem to be appropriate or not. Between-subjects design a design in which different subjects take part in each condition of the experiment. Block randomization a process of randomization that first creates treatment blocks containing one random order of the conditions in the experiment; subjects are then assigned to fill each successive treatment block. Control condition A condition in which subjects reccive a zero value of the independent variable. Control group The subjects in a control condition. Effect size A statistical estimate of the size or magnitude of the treatment effects). Experimental condition A treatment condition in which the researcher applies a particular value of an independent variable to subjects and then measures the dependent variable; in an experimental group-control group design, the group that receives some value of the independent variable. Experimental design the general structure of an experiment (but not its specific content). Experimental group the subjects in an experimental condition. Pilot study A mini-experiment using only a few subjects to pretest selected levels of an independent variable before conducting the actual experiment. Placebo group In drug testing, a control condition in which subjects are treated exactly the same as subjects who are in the experimental group, except for the presence of the actual drug; the prototype of a good control group. Precision matching Creating pairs whose subjects have identical scores on the matching variable. Random assignment The technique of assigning subjects to treatments so that each subject has an equal chance of being assigned to each treatment condition. Range matching Creating pairs of subjects whose scores on the matching variable fall within a previously specified range of scores. Rank-ordered matching Creating matched pairs by placing subjects in order of their scores on the matching variable; subjects with adjacent scores become pairs. Two-experimental-groups design A design in which two groups of subjects are exposed to different levels of the independent variable. Two-group design The simplest experimental design, used when only two treatment conditions are needed. Two-independent-groups design An experimental design in which subjects are placed in each of two treatment conditions through random assignment. Two-matched-groups design An experimental design with two treatment conditions and with subjects who are matched on a subject variable thought to be highly related to the dependent variable. Thank you for listening!