Analyzing and Interpreting Quantitative Data Chapter 6 Analyzing and Interpreting Quantitative Data This multimedia product and its contents are protected under copyright law. The following are prohibited by law: any public performance or display including transmission of any image over a network; preparation of any derivative work, including the extraction, in whole or in part, of any images; any rental, lease, or lending of the program.

By the end of this chapter, you should be able to: Identify the steps in the process of analyzing and interpreting quantitative data Describe the process of preparing your data for analysis Identify the procedures for analyzing your data Learn how to report the results of analyzing your data Describe how to interpret the results

Steps in the Process of Quantitative Data Analysis Preparing the data for analysis Conducting the data analysis Reporting the results Interpreting the results

Preparing the Data for Analysis: Inputting Data Score data by assigning numeric codes to responses Create a codebook Use information from instruments when possible as a part of coding scheme Create a data file in data grid Create variable, value labels Clean database, missing values

Determine Types of Scores to Analyze Single item Summed scores Difference scores

Selecting a Statistical Program Statistical Package for Social Sciences (SPSS) most popular Other programs Minitab JMP SYSTAT SAS

Conducting Descriptive Analysis Measures of central tendency (value or score that represents the entire distribution) Mean: Typically called the “average” Median: The value or score that divides the top half of a distribution from the bottom half Mode: The value or score that occurs most often Measures of variability (describes the “spread” of the scores Range: The difference between the highest and lowest scores Standard deviation: The standard distance the scores are away from the mean

Conducting Descriptive Analysis Measures of relative standing Percentile rank: The percentage of participants in the distribution with scores at or below a particular score Calculated score: Enables a researcher to compare scores from different scales Z-Score: A popular form of the standard score, has a mean of 0 and a standard deviation of 1

Descriptive Statistics Central Tendency Variability Relative Standing Mean Median Mode Variance Standard Deviation Range Z-Score Percentile Ranks

Inferential Statistics Analysis of Variance Chi-Square Pearson Correlation Multiple Regression T-test

Conducting Inferential Analysis Hypothesis testing: A procedure for making decisions about results by comparing an observed value of a sample with a population value to determine if no difference or relationship exists between the values Confidence interval: The range of upper and lower statistical values that is consistent with observed data and is likely to contain the actual population mean

Conducting Inferential Analysis (cont’d) Effect size: A means for identifying the practical strength of the conclusions about group differences or about the relationship among variables

Conducting Hypothesis Tests Identify a null and alternative hypothesis Set the level of significance (alpha level) for rejecting the null hypothesis Collect the data Compute the sample statistic Make a decision about rejecting/failing to reject

Selecting an Appropriate Statistic Determine the type of quantitative research question or hypothesis you want to analyze Identify the number of independent variables Identify the number of dependent variables Identify whether covariates and the number of covariates are used in the research question or hypothesis

Selecting an Appropriate Statistic Consider the scale of measurement for your independent variable(s) in the research question or hypothesis Identify the scale of measurement for the dependent variables (e.g., continuous or categorical) Determine if the distribution of the scores is normal or skewed

Normal Curve 34% 34% 13.5% 13.5% 2.5% 2.5% Mean -3 -2 -1 +1 +2 +3 Standard Deviations

The Normal Curve of Mean Differences of All Possible Outcomes If the Null Hypothesis Is True Reject the Null Hypothesis Reject the Null Hypothesis High Probability Values If the Null Hypothesis Is True Extremely Low Probability Values If Null Hypothesis Is True (Critical Region) Extremely Low Probability Values If Null Hypothesis Is True (Critical Region) Alpha=.025 Alpha=.025 Two-Tailed Test

Outcomes of Hypothesis Testing: Type I and Type II Errors Decision Made by the Researcher Based on the Statistical Test Value State of Affairs in the Population No Effect: Null True Effect Exists: Null False Type I Error (false positive) (probability = Alpha) Correctly rejected: no error (probability = power) Reject the Null Hypothesis Correctly not rejected: no error Type II Error (false negative) (probability = Beta) Fail to Reject the Null Hypothesis

Reporting the Results Tables summarize statistical information Title each table Present one table for each statistical test Organize data into rows and columns with simple and clear headings Report notes that qualify, explain, or provide additional information in the tables, which can be helpful to readers. Notes include information about the size of the sample reported in the study, the probability values used in hypothesis testing, and the actual significance levels of the statistical test

Reporting the Results (cont’d) Figures (charts, pictures, drawings) portray variables and their relationships Labeled with a clear title that includes the number of the figure Augment rather than duplicate the text Convey only essential facts Omit visually distracting detail Easy to read and understand Consistent with and are prepared in the same style as similar figures in the same article Carefully planned and prepared

Reporting the Results (cont’d) Detailed explanations about statistical results Report whether the hypothesis test was significant or not Provide important information about the statistical test, given the statistics Include language typically used in reporting statistical results

Discussing the Results Summarize major results Explain why they occurred Explain the implications of the results for the audiences Advance limitations Suggest future research End on positive note