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Hypothesis

statement about the proposed relationship between two or more variables.

Hypothesis Process includes __ steps:

Six
1)State the hypothesis 2) Set a level of risk 3) Choose sample size 4) Determine critical value 5) Compute test statistic 6) Accept or reject hypothesis 
Descriptive Statistics

Summarize or describe data. Do not make inferences about the population.

Inferential Statistics

Make conclusions about the population from data. Make inferences

When stating the hypothesis, researchers consider two opposing viewpoints

Null Hypothesis and Alternative Hypothesis.

Null Hypothesis

States that there is no relationship between the groups or variables.

Alternative Hypothesis

States that there will be a difference between the groups relative to the specific variable examined.

Alpha

The probability of a type 1 error.

Beta

The probably of a type 2 error.

Power

The probability that your research will correctly reject the null hypothesis when it is false.

How can the critical value be found?

Using a table for standard normal distribution and student t distribution.

The critical value depends on:

alpha level and alternate hypothesis.

Two types of alternative hypothesis?

One tailed and Two Tailed

Test Statistic

Statistical Test used to analyze data.

Distribution

A pattern of scores: distribution of a variable provides information about individual cases as well as info about the group of scores.

Normal Distribution has 3 important characteristics

1)Symmetrical and uni modal.
2) Continuous 3) Asymptomatic 
Standard Normal Distribution

All normal distribution can be converted to a common distribution with the same mean and standard deviation (mean of 0 and standard deviation of one).

Transformed Standard Scores

Z scores include negatives and decimals so you would transform scores into a distribution of standard scores with a mean of 100 and SD of 15.

ZScore

Standard score. A measure of individual location telling us where individual scores are located within a distribution of scores.

Negatively Skewed

Larger values to the right tail.

Positively Skewed

Large values towards the left tail.

Kurtosis

Measure of peakedness: tells us how fat or thin tails are relative to normal distribution.

Central Limit Theorem

Even if variable is not normally distributed in the population, the sampling distribution will be approximately normal with a large sample.

Interval Estimation

Range of values that you can say with confidence include the population parameter; range of values is confidence interval.

Student T Distribution

Bell shaped, symmetrical, centered on the mean however distribution change as sample sizes change.

Majority of quantitative analysis in CSD are:

Student T Test and ANOVA (these are inferential statistical procedures)

Related Samples Test

Test that matches pre and post test designs for difference between two conditions. Same participants are observed before and after treatment.

Random Sampling

Each member of population has an equal chance of being selected.

Nonparametric Statistic Example

Chi Square Test

Unrelated Samples Test

2 groups of subjects are partcipants. Examine differences between groups by comparing the means.

Z statistic

Statistic that is appropriate for larger samples (more than 30). Based on normal distribution of scores.

T statistic

Statistic that is appropriate for smaller samples (less than 30)

Leptokurtic

Narrower in terms of bellshaped curve.

T ratio

difference between the means of 2 groups divided by the SED.

Standard Error of Difference

Variability of Scores due to error.

Nonparametric Test for two Unrelated Samples

Mann Whitney U

Nonparametric Test for Related Sample

Wilcoxon Test for Paired Data.

The most common approach to statistical analysis in CSD research is:

ANOVA

ANOVA outcome is __ Statistic

F Statistic

ANOVA separates Variance into:

Within Group Variability
Between Group Variability 
Within Group Variability

Portion of Total Variance that cannot be explained by research design.

Between Group Variability

Portion of Total Variance that can be attributed to group membership.

Nonparametric Alternative to One Way ANOVA is:

Kruskal Wallis Test

In Complex ANOVA Procedures, researchers evaluate what effects?

Main Effects and Interaction Effects.

Main Effects

Effects of the Variables.

Interaction Effects

Interaction between the variables.

Significant Interaction

One variable is influencing another variable.

Post Hoc Comparisons

Analyze pairs of means for significance.

Nonparametric Alternative to twoway ANOVA is:

Friedman's Test

Contingency Tables

rows by columns, tables used to organize data in chi square analysis.

Phase 1 of Clinical Outcome Research

Exploratory. Tentative Treatment Protocol. Lacking external controls.

Phase 2 of Clinical Outcome Research

Exploratory. Finalizing operational definition, defining population of interest, refining methodology. Exploring treatments effects in terms of extent and maintenance.

Phase 3 of Clinical Outcome Research

testing research hypothesis and answering research question. Study done with larger samples and control group added.

Phase 4 of Clinical Outcome Research

Bridge between research and practice. Going from efficacy in the lab to effectiveness in the clinical setting.

Phase 5 of Clinical Outcome Research

cost benefit ratio, consumer satisfaction, quality of life issues.

Synthesis Review Approaches

Narrative Review and Qualitative Analysis

Narrative Review

Thorough search of pertinent literature. Qualitative analysis of results of past studies. Conclusion based on synthesis of results.

Quantitative Review

Existing Literature is Sufficient in number and type thus often superior to narrative review.

Quantitative Review Methods

Vote Counting Method
Combined Probability Method 
Vote Counting Method

results of selected studies are placed into categories: positive, negative, nonsignificant. Category with largest population of finding is identifies as supporting/refuting research hypothesis.

Combined Probability Method

Probability included in synthesis, do not quantify size of experimental effects, and do not identify heterogeneity among studies.

Modern Meta Analysis

Effect Size Combined, Overall measure of effect, significance of overall effects.

Modern Meta Analysis Step 1

1) Develop a research hypothesis and eligibility criteria.

Modern Meta Analysis Step 2

Develop a search strategy and choose studies for inclusion.

Modern Meta Analysis Step 3

Convert test statistic to common effect size metric.

Modern Meta Analysis Step 4

Compute cumulative effect and interpret results.

Moderator Variable

independent variable other than the treatment variable that can explain significant amount of variance between studies.

Statistical Models to represent data

Fixed Effects and Random Effects

Fixed Effects Model and Example

Variability of results between studies is due to random variation alone. Randomized Controlled Trials.

Random Effects Model and Example

Variability of results between studies is due to random variation and other confounding variables such as experimenter bias and others. Quasi Experimental Research
