CHAPTER 7 SingleFactor Independent Groups Designs Copyright 2005

CHAPTER 7 Single-Factor, Independent. Groups Designs Copyright 2005, Prentice Hall, Sarafino

Single-Factor, Independent. Groups Designs • A Single-Factor, Independent-Groups Designs is one that: – Has only one independent variable (IV) – The IV has two or more levels – Separate groups are expose to only one level of the IV and compared • These designs are also referred to as independent groups designs or betweensubjects designs. Copyright 2005, Prentice Hall, Sarafino

Two-Level, Single-Factor Designs • A two-level, single factor design is one that has a single independent variable (IV) and the IV has two levels. – One of the levels is often a control condition. – If one level is not a control then the design has a serious weakness. – E. g. , IV = Drug, and this Drug IV has 2 levels – a no drug level and a drug level. Copyright 2005, Prentice Hall, Sarafino

Two-Level, Single-Factor Designs: Non-Manipulated IV • Recall that a non-manipulated IV is often a subject variable (e. g. , gender, SES). – Subject variables give rise to non-equivalent groups and as such are considered quasiexperimental designs. Copyright 2005, Prentice Hall, Sarafino

Maximizing IV Variance • Recall that researchers try to maximize variance from the levels of the IV and try to minimize variance from extraneous variables and nonsystematic error. • To maximize variance from the IV researchers typically select levels for the IV that are very different (e. g. , no dose, high dose). – How do researchers know what levels to choose? • Research! • Trial and Error • Common sense, predictions, theories. Copyright 2005, Prentice Hall, Sarafino

Minimizing Other Variance • How can researcher minimize error variance and variance from extraneous variables? – The answer is simple – by being careful. – Things to do to minimize unwanted variance: • Be sure to create equal groups (use randomization, matching, or repeated measures) • Guard against differential attrition (dropout) • Watch for history, maturation, and regression to the mean. • Double blind procedure. • Keep experimental conditions constant. Copyright 2005, Prentice Hall, Sarafino

Control Groups • You’re familiar with the concept of a control group: A group that is not exposed to the IV and serves as a baseline comparison. • Let consider some control group variants: Copyright 2005, Prentice Hall, Sarafino

Placebo Control Group • Placebo control groups are typically use in drug studies. • A placebo control group receives identical treatment to that of the experimental condition except the substance they receive is inert. – Placebo groups allow for false expectations – which may themselves be beneficial. • The placebo effect is any effect the placebo condition has on the dependent variable. – Placebos have been shown effective in pain reduction – they likely cause the release of the body’s own endogenous opioids. Copyright 2005, Prentice Hall, Sarafino

Yoked Control Groups • • • A yoke is a frame that links two animals, at the neck, together to equalize work. A Yoked Control Group means that each member of a pair of participants experiences simultaneously the factors in an experiment – the control participant does not receive the IV manipulation. The key feature here is the experience of the same events, expect for the IV, at the same time. Copyright 2005, Prentice Hall, Sarafino

Waiting List Control Group • A waiting list control group is exactly what it appears to be. Persons in the waiting list control condition do not initially get exposed to the IV, but after some delay they will be exposed to the IV. • Two key features: – The effect of a IV can be compared to a standard group of similar individuals – Control participants eventually get exposed to IV and if the IV is a life-saving treatment they will eventually get the treatment. Reduces the ethical dilemma of not treating someone – especially if the treatment works. Copyright 2005, Prentice Hall, Sarafino

Multilevel, Single-Factor Studies • The designs have a single IV, but more that 2 levels of the IV exist. – E. g. Drug Dose with four levels (0 g, 0. 01 g, 0. 05 g, 0. 1 g). • Why use a multilevel design? – See if a nonlinear effect exists – The hypotheses may require more than 2 levels to be supported. Copyright 2005, Prentice Hall, Sarafino

Analyzing Data: Single-Factor Independent-Groups Designs Two-Level Designs Parametric Analysis • Must have interval or data. • Statistical procedure: independent-groups t-Test – t-Test assesses the mean differences between two groups. Nonparametric Analysis • Must have nominal or ordinal data • Statistical Procedure: – – If nominal: Chi-Square Test If ordinal: Mann-Whitney U Copyright 2005, Prentice Hall, Sarafino

The p Value • The p value is the likelihood that the difference you found might have occurred if no difference actually does exist. • Generally speaking, if the p value is less than 0. 05 we reject the null hypothesis and conclude that the difference we found was real. – A p value less that 0. 05 means that there is less that a 5% chance that the results you obtained occurred by chance. Copyright 2005, Prentice Hall, Sarafino

Effect Size • Effect size is a measure of the separation between two populations. – Put another way, effect size is the effect that the experimental procedure had on separating the experimental group from the control group. • There are various statistics used to estimate effect size and all are based on correlation. Some examples of effect size correlations include: – Cohen’s d – Cramer’s V – r. ES: t Copyright 2005, Prentice Hall, Sarafino

Multilevel Design: Statistics Parametric Analysis • The most common statistical procedure for a multilevel design with interval or ratio dependent measures is an analysis of variance (ANOVA). • There are various forms of ANOVA – when only one independent variable exists with more that 2 levels, a “one-way ANOVA” is typically used. • One-way ANOVAs determine if the differences observed in the sample means are large enough to draw a conclusion that the population means are different. – Interestingly, the way we test this is to measure the variance amongst the means. Copyright 2005, Prentice Hall, Sarafino

Multilevel Design: Statistics Nonparametric Statistics • If your data from a multilevel design is nominal then a chi-square test is used. • If your data is ordinal then a multilevel design requires either a: – Kruskal-Wallis H test, or a – K-sample median test. Copyright 2005, Prentice Hall, Sarafino