Factor Design and Within subjects design

• Designs in which we study two or more independent variables at the same are called factorial designs. The independent variables in these designs are called factors and each factor will have two or more values or level*.
• They give us information about the effects of each independent variable in the experiment, called main effects*.
• It answers the question: How much did the change in this one independent variable change the subject’s behaviour.

  Main Effect
• A main effect is simply a change in behaviour associated with a change in the value of a single independent variable within the experiment. There are as many main effects as there are factors.
• An interaction is present if the effect of one independent variable changes across the levels of another independent variable. Whenever an interaction is present in a two-facto experiment, we cannot get a complete picture of the results of the experiment without considering both factors because the effects of one factor will change depending on the levels of the other.
• The simplest factorial design has only two factors and is called a two- factor experiment.
• According to Zinser (1984), the following are the advantages of the factorial design
• Factorial design

  • Allows investigators to study the influence of several factors at one time
  • Provides investigators with the potential to understand a larger segment of life which is possible with single-factor design
  • May be more economical in time and effort than conducting two or more studies independently
• Factorial design

  • Allows investigators to study the interaction between variables
• Interaction between variables

  One variable influencing the effect of another variable, that is, the effect of one variable depending upon the conditions of another variable
• A shorthand notation is a system that uses numbers to describe the design of a factorial design
• HYPOTHESIS: Subjects will be drowsier when they had taken both alcohol (drug) and sleeping pill than taking only either of the two. How many levels of IV?

  4
• Study the table

  A) Factorial Design

  B) Number of Independent Variable

  C) Number of Conditions of Variable

  D) Number of Groups

  
• Within-subjects design is a design in which each subject serves in more than one condition of the experiment. We make comparisons of the behaviour of the same subjects under different conditions.
• By having the same subjects in both attention conditions, Jones and her colleagues improved their chances of detecting differences between the attention and nonattention conditions. From a statistical viewpoint, refer to this as increasing power of the experiment.
• Increased power means a greater chance of detecting a genuine effect of the independent variable
• In a within-subjects design, subjects serve in more than one condition of the experiment and are measured on the dependent variable after each treatment; thus, the design is also known as a repeatedmeasures design.
• Within-subjects factorial can require much fewer subjects than a between subjects design that is testing the same hypothesis.

  True
• Order effects refer to differences in research participants’ responses that result from the order (e.g., first, second, third) in which the experimental materials are presented to them.
• A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions.
• fatigue effects where participants perform differently at the end of an experiment or survey because they are bored or tired.
• Practice effects can also occur when participants warm up or improve their performance over time.
• These effects can create Progressive Error. These are changes in participant responses that are caused by testing in multiple treatment conditions; includes order effects, such as the effects of practice or fatigue.
• In a within-subjects experiment, we cannot eliminate order effects. Nor can we hold them constant, giving all subjects the treatments in the same order because we are trying to avoid just this kind of systematic effect. But we can balance them out –distribute them across the conditions – so that they affect all conditions equally.
• Subject-by-subject counterbalancing is a technique for controlling progressive error for each individual by presenting all treatment conditions more than once.
• Reverse Counterbalancing -It is a technique for controlling progressive error for each individual subject by presenting all treatment conditions twice, first in order then in the reverse order. Ex. ABBA
• Block Randomization -Each set of treatments (e.g. ABCD) is considered a block, and treatments within each block are given in random order. For block randomization to be successful, it is usually necessary to present each treatment several times, resulting in a sequence containing a number of randomized blocks.
• Complete counterbalancing controls the effects of progressive error by suing all possible sequences of conditions and using every sequence the same number of times.
• Across-subject counterbalancing makes sure that participants receive different order of treatments once in the experiment. This minimizes fatigue on the side of the participants.
• What type of across-subject counterbalancing is demonstrated?

  Complete Counterbalancing
• The Latin Square design involves four levels or factors with three replications per level. The design has no carryover effect because each factor appears only once in any row or column.
• Partial Counterbalancing -We use partial counterbalancing when we cannot do complete counterbalancing but still want to have some control over progressive error across subjects.
• Randomized partial counterbalancing -When there are many possible sequences, we randomly select out as many sequences as we have subjects for the experiment.
• Latin square counterbalancing -A matrix, or square, of sequences is constructed that satisfies the following condition: (1) Each treatment appears only once in any order position in the sequences and (2) each row represents a different order sequences.
• Order effects emerge as a result of the position of a treatment in a sequence.
• In order to minimize the carryover effect. We have to perform a balanced Latin square. A balanced Latin square is a matrix, or square, of sequences is constructed that satisfies the following condition:

  1. Each treatment appears only once in any order position in the sequences and
  2. 2. Each treatment precedes and follows every other condition an equal number of times.
• ADVANTAGES OF REPEATED-MEASURES DESIGN:

  1. It uses participant more economically.
  2. It saves laboratory time.
  3. It reduces error variance.
• DISADVANTAGES OF REPEATED –TREATMENT DESIGN:

  1. The treatment effects may not be reversible
  2. There may be order effects
  3. There may be contradictory results
• MIXED DESIGN -A design that combines within- and between- subjects variables in a single experiment is called a mixed design. This means that we can use a factorial design that combines one factor that is manipulated within subjects (such as the four types of expressions) with a between subjects factor (often a subject variable, such as gender or age of the subjects) that cannot be manipulated by an experiment.
• when two independent variables are present, there is only one possible interaction; the picture is a bit more complicated when we have the same subjects in more than one treatment condition of the experiment.