Research Methods Independent Groups Design PDF

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Independent Group Design Experiment Definition ● Type of research design that involves manipulation of an independent variable, allowing for control of extraneous variables that can affect the results Experimental Research ● Must include IV & DV ● Independent Variable ○ Manipulated (controlled) by experimenter ○ At least 2 conditions (levels) ■ “Treatment” and “Control” ● Dependent Variables ○ Measured by experimenter ○ Used to determine effect of IV ■ Typically researchers measure several dependent variables to assess effect of IV Internal Validity (Revisited) ● Differences in performance (DV) can be attributed unambiguously to effect of independent variable (IV) ● 3 conditions for causal inference ○ Covariation ○ Time-order relationship ○ Eliminate alternative causal explanations (confoundings) Dittmar, Halliwell, & Ives (2006) ● Research question ○ Will young girls exposed to a very thin body image experience greater body dissatisfaction than young girls who are exposed to realistic or neutral body images? ● IV: version of picture book with 3 levels ○ Barbie (very thin body image) ○ Emme (realistic body image) ○ Neutral (no body images) ● Dependent Variables ○ Several measures of body image and body dissatisfaction ○ Child Figure Rating Scale ■ Rate perceived actual body shape ■ Rate ideal body shape ■ Obtain difference score ● Negative score: desire to be thinner ● Control Techniques ○ Manipulation ■ IV: participants in the conditions have different experiences (Ex. Barbie, Emme, or neutral images) ○ Holding conditions constant ■ IV is only factor that differs systematically across groups ● All girls listened to same instructions and story ● All completed the same questions after the story ○ Balancing ■ Random assignment balances subject characteristics

■ Groups are equivalent prior to IV manipulation ■ All subject variables are balanced ● Body weight, number of Barbie dolls, preexisting levels of body dissatisfaction, etc. Independent Groups Designs ● Different individuals participate in each condition of the experiment. ○ No overlap of participants across conditions ● Three types ○ Random groups design – most effective ○ Matched groups design ○ Natural groups design Random Groups Designs ● Individuals are randomly assigned to conditions of the IV ● Logic of causal inference ○ If groups are equivalent at the beginning of an experiment (through balancing) and conditions are held constant, any differences among groups on dependent variable are caused by the manipulated independent variable. Ways to Randomize ● True randomization ● Block randomization ○ Block: random order of all conditions in the experiment ○ Randomly assign subjects 1 block at a time ○ Advantages ■ Creates groups of equal size ■ Controls for time-related events that occur during course of experiment ● Events that occur in the environment ● Changes in experimenters ● Changes in populations from which subjects are drawn Threats to Internal Validity in Independent Groups Designs ● Testing intact groups ● Extraneous variables ○ Hold conditions constant ● Subject loss ○ Equal groups can become unequal ○ Mechanical vs. selective ○ Preventative approaches? ● Demand characteristics ○ Experimenter effects ○ Use placebo-control and double-blind procedures Analysis and Interpretation of Experimental Findings ● Use statistical analysis to: ○ Claim IV produced an effect on DV ○ Rule out the alternative explanation that chance produced any observed effect ● Replication ○ Best way to determine whether findings are reliable ○ Repeat experiment and see if same results are obtained

Data Analysis: 3 Steps ● Check the data ○ Errors? Outliers? ● Describe the results ○ Descriptive statistics ■ Measures of central tendency (e.g., mean, median, mode) ■ Measures of variability (e.g., SD, range) ○ Effect sizes (e.g., Cohen’s d, eta squared) ● Hypothesis testing ○ Inferential statistics – confirm what the results reveal ○ How large was the effect? Describing Results ● Descriptive Statistics ○ Mean (central tendency) ■ Average score on DV, computed for each condition ■ Not interested in each individual score, but how people responded on average in a condition ○ Standard deviation (variability) ■ Average distance of each score from the mean of a group ■ Not everyone responds the same way to an experimental condition ○ Effect size ■ Measure of strength of relationship between the IV and DV ■ Cohen’s d ● difference between treatment and control means ● average variability for all participants’ scores ● Guidelines for interpreting Cohen’s d: ○ small effect of IV: d = .20 ○ medium effect of IV: d = .50 ○ large effect of IV: d = .80 Hypothesis Testing ● Confirm what the data reveal ○ Use inferential statistics to determine whether the IV produced a reliable effect on the DV ○ Rule out whether findings are due to chance (error variation) ○ Two types of inferential statistics ■ Null Hypothesis Significance Testing ■ Confidence intervals ● Null Hypothesis Significance Testing ○ Definition: Statistical procedure to determine whether mean difference between conditions is greater than what might be expected due to chance (error variation) ○ Effect of an IV on the DV is statistically significant when the probability of the observed results being due to chance is low ○ p < .05 ● Steps for Null Hypothesis Testing ○ Assume the null hypothesis is true ○ Use sample means to estimate population means ■ mean body dissatisfaction for Barbie = -.76

■ mean body dissatisfaction for Emme = 0.00 ■ mean body dissatisfaction for neutral = 0.00 ■ difference between Barbie and Emme/neutral = -.76 ○ Compute the appropriate inferential statistic ○ Identify the probability associated with the inferential statistic ○ Compare the observed probability with the predetermined level of significance (usually p < .05) ● Confidence Intervals ○ Sample means estimate population means ○ Confidence interval for a mean provides the range of values that contains the true population mean. ■ with some probability, usually .90 or .95 ● Steps for Interpreting Confidence Intervals ○ Compute confidence interval around sample mean in each condition. ■ If confidence intervals do not overlap, we gain confidence that population means for the conditions are different--that is, the IV has an effect ■ If confidence intervals overlap slightly, we are uncertain about the true mean difference ■ If intervals overlap such that the mean of one group lies within interval of another group, we conclude the population means do not differ ○ Errors ■ Type 1: we reject the null hypothesis and claim that an outcome in statistically significant, when the null hypothesis is actually true (false alarm) ■ Type 2: we conclude we have insufficient evidence to reject the null hypothesis but in fact there is statistical significance Meta-Analyses ● Summarize effect sizes across many experiments that investigate same IV or DV ● Choose experiments based on their internal validity and other criteria ● Allows researchers to gain confidence in general psychological principles External Validity ● Definition: Extent to which findings from an experiment can be generalized to describe individuals, settings, and conditions beyond the scope of a specific experiment ○ Any single experiment has limited external validity ○ External validity of findings increases when findings are replicated in a new experiment ● Must ask ourselves: ○ Would the same findings occur… ■ In different settings? ■ In different conditions? ■ With different participants? Increasing External Validity ● Include characteristics of situations, settings, and population to which researchers seek to generalize ● Partial replications ● Conceptual replications ● Field experiments Additional Independent Groups Designs ● Different individuals participate in each condition of the experiment. ○ No overlap of participants across conditions ● Three types

○ Random groups design – most effective ○ Matched groups design ○ Natural groups design Matched Groups Design ● Random assignment requires large samples to balance subject characteristics ● Sometimes only small samples are available ● In matched groups design researchers select 1 or 2 individual differences variables for matching ● Procedure: ○ Select matching variable ■ related to outcome or dependent variable ○ Match pairs (or triples, quadruples, etc. depending on number of conditions) of identical or similar scores ○ Randomly assign participants within each match to the different IV conditions ○ Groups not equivalent for all individual differences variables Natural Groups Design ● Sometimes we are interested in how individual differences are related to important outcomes ○ Individual differences (subject variables): characteristics or traits that vary across individuals (e.g., male, female) ● Experiments in which IV levels are selected (and not manipulated) involve natural group designs ○ e.g., religion, ppl diagnosed with depression, ppl who have divorced parents ● Can’t randomly assign participants to these groups ● Classify individuals into groups based on subject variable, then measure DVs ● Select individual differences IVs ● Correlational research ● Describe and predict using relationships between natural groups variable and DVs ● BUT we can’t infer causality b/c we have not eliminated plausible alternative causes ○ Individual differences are usually confounded...