DESIGNING, CONDUCTING, ANALYZING & INTERPRETING DESCRIPTIVE RESEARCH CHAPTERS 7 & 11 Kristina Feldner

Types of Quantitative Descriptive Research Descriptive Causal Comparative Correlational

DESCRIPTIVE RESEARCH AKA SURVEY RESEARCH Describe features of or provide a picture of a condition or phenomenon using quantitative data Purpose Administer written survey/questionnaire/interview Document responses Analyze data Describe participant attitudes, beliefs, or behaviors Process Quantitative Outcomes Tally frequencies, percentages, mean scores Outcomes Typically rely on responses of participants to specific questions Misc.

CAUSAL COMPARATIVE RESEARCH & EX POST FACTO Identify potential cause-and-effect relationships between targeted groups of individuals Attempts to answer question, “Is there a causal relationship?” Purpose Researchers test the relationship of independent and dependent variables Process Categorical data are nominal, ordinal, or ratio Outcomes Independent variable is not a treatment or condition Variables not controlled ahead of time Potentially misleading Misc.

CORRELATIONAL RESEARCH Describe or analyze relationships between variables, conditions, or events Provides an indication of whether or to what degree two or more variables are related or how one thing may be predicted from another Attempts to answer the question, “What is the magnitude or degree of relationship?” Purpose Data collected through surveys, questionnaires, or interviews May also use existing databases Process Quantitative index of the strength of relationships = Correlation Coefficient Outcomes Variables are not manipulated Study can provide information about where to base future research Allows researcher to explore relationships among a greater number of variable at one time Analyze data with caution Misc.

ANALYZING & INTERPRETING DESCRIPTIVE RESEARCH

DESCRIPTIVE DATA DISPLAYS Provides information about how often a certain response occurred or the percentage of responses that the frequency indicates Frequency Tables Illustrate the relationship between two variables whose measure yield interval or ration data, or continuous scores. Histograms

CAUSAL COMPARATIVE RESEARCH Select a Statistic t test Analysis of variance (ANOVA) Chi-Square analysis Interpret results of Analysis Level of significance F statistic degrees of freedom (df) Discuss Results Interpret results with caution Don’t generalize results to greater population Causation can only be suggested Step 1: Compute basic descriptive statistics Step 2: Test statistical significance of differences between groups in relation to the dependent variable.

CORRELATIONAL RESEARCH Analyze strength of relationship between 2 or more variables Choice of statistic depends on type of data Select a Statistic Correlation coefficient Pearson r Spearman rho Multiple regression Interpret results of Analysis Depends on analysis you run an software used ** tell you that a value is statistically significant Discuss Results Scatter Plot Results interpreted with caution Researcher will discuss Relationships between variables Limitations of study Implications for practice Future research needs