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Lecture 18

APSY 2216 Lecture 18: Mar 31_Research Methods and Analyses_Thomson Premium

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School
Boston College
Department
Counseling, Developmental, and Educational Psychology
Course
APSY 2216
Professor
Thomson
Semester
Spring

Description
ANNOUNCEMENTS ● Today and next week: quantitative data analysis and review for exam 2 ● Remaining assignments: Research Plan due today and Research in Real Life 3 due April 21 ● Topics covered since Exam 1 ○ Ethical research ○ Experimental research ○ Associational research: causal-comparative and correlational ○ Qualitative research ○ Survey research ● Exam 2: Wednesday, April 12 QUANTITATIVE DATA ANALYSIS ● In quantitative research, we collect data from a sample but are interested in a broader population ○ No guarantee that a particular sample is representative of the population ○ Participants’ performance on any given day can be influenced by a variety of factors beyond our control (lack of sleep, not paying attention, etc.) ● Statistics provide us with the tools that allow us to bridge the gap between the sample and population ○ Allow us to better understand patterns that we observe in a sample (descriptive statistics) ○ Allow us to figure out how much of what we observe in a sample is truly reflective of the population and how much is due to random chance (inferential statistics) ● Descriptive statistics - summarize sample data and describe general trends in the data ● Inferential statistics - draw inferences from a sample that can be generalized to the population ● Categorical data - obtained by determining the frequency of occurrences in each of several categories ● Continuous data - obtained by determining placement on a scale that indicates amount or degree ● Types of scores - quantitative data are often reported in the form of scores ○ Raw scores - the initial score → taken by itself, it is difficult to interpret, since it has little meaning ○ Derived scores - scores that have been taken from raw scores and standardized → enable researchers to say how well the individual performed compared to others taking the same test ■ Ex: percentile rank, age and grade-level equivalents ○ Standard scores are mathematically derived scores having comparable meaning on different instruments (ex: compare percentiles on MCAS and PSAT) ● Frequency distribution - show the frequency with which each different score occurs ○ One of the most common ways of summarizing quantitative data ○ Displays chaos of numbers in an organized manner ○ Graphical displays of frequency distributions ■ Polygon - line graph ■ Histogram - bar graph ■ Stem-leaf plot - looks like a histogram, but instead of bars, it shows values for each category ■ Normal distribution - most scores are clustered around the mean, relatively few are at both extremes, typical of physical and psychological variables (height, weight, anxiety, IQ, etc.) ■ Negatively skewed - most scores are high (ex:
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