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PSYC 2360 (109)
Lecture

Chapter 12.docx

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Department
Psychology
Course
PSYC 2360
Professor
Dan Meegan
Semester
Summer

Description
Chapter 12: Understanding Research Results: Description and Correlation Statistics helps us understand the data collected in research investigations.  Statistics are used to describe a data  Statistics are used to make inferences, on the basis of sample data, about a population Scales of Measurements: A Review  Variables can be described using one of four scales of measurements: 1. Nominal o Have no numerical, quantitative properties. The levels are simply different categories or groups o Most independent variables such as gender, eye colour, and hand dominance are nominal 2. Ordinal o Involve minimal quantitative distinctions. The levels are rank ordered from lowest to highest. o For example rank-ordered judgements of most important problems facing your state today. Although you may get an order (1 crime, 2 health, 3 crime) but, you do not know how strongly people feel about the problems; the intervals between each of the problems are probably not equal 3. Interval o More detailed quantitative properties. The intervals between levels are equal in size. The difference between 1 and 2 is the same as the difference between 2 and 3. o There is no absolute zero point that indicates an “absence” of the variable being measured. 4. Ratio o More detailed quantitative properties. The intervals between levels are equal in size and there is an absolute zero point that indicates an absence of the variable being measured. Example would be time, weight, length and other physical measures.  The scale used determines the type of statistics that are appropriate when the results of a study are analyzed. The meaning of a particular score on a variable depends on which type of scale was used when variable was measured or manipulated. Analyzing the Results of Research Investigations  Most research focuses on the study of relationships between variables. Depending on the way that the variable are studied, there are three basic ways of describing the results: 1. Comparing Group Percentages o For example when trying to find a relationship between gender and travel. The percentage of males who like travelling is compared to percentage of females who like travelling.  Note: We are focusing on percentage because the travel variable is nominal: Liking and disliking are simply two different categories 2. Correlating Individual Scores o A second type of analysis is needed when you do not have distinct groups of subjects. Instead, individuals are measured on two variables, and each variable has a range of numerical value.  Relationship between location in a classroom and grades in the class. 3. Comparing Group Means o Used in research to compare the mean responses of participants in two or more groups. For example studying the effect of exposure to an aggressive adult.  One group of children get exposure and the other does not and both groups are left to play alone and aggressive behaviour is recorded during observation.  Aggression as a ratio scale because there are equal intervals and a true zero on the scale Frequency Distributions  A frequency distribution indicates the number of individuals that receive each of the possible score on a variable. Graphing Frequency distributions 1. Pie Charts o Divide a whole circle or “pie” into “slices” that represent relative percentages o Useful when representing nominal scale information o Are frequently used in applied research reports and in articles in newspapers and magazines. 2. Bar Graphs o Use a separate and distinct bar for each piece of information 3. Frequency Polygons o Use a line to represent frequencies. o Useful when the data represent interval or ratio scales 4. Histograms o Uses bars to display a frequency distribution for a quantitative variable. o The scale values are continuous and show increasing amounts on a variable (e.g. age, blood pressure), and because the values are continuous, the bars are drawn next to each other o By looking at histogram you can tell: the number of respondents, frequent scores, shape of the distribution of the scores and outliers (scores that are unusual, unexpected, or very different from the scores of other participants. Descriptive Statistics o Descriptive statistics allows researchers to make precise statements about the data and two statistics are needed to describe the data: 1. Central Tendency  A single number can be used to describe the central tendency, or how participants scored overall, the sample as a whole.  There are three measures of central tendency:  Mean  Is obtained by adding all the scores and dividing by the number of scores  It is an appropriate indicator of central tendency only when scores are measured on an interval or ratio scale, because the actual values of the numbers are used in calculating the statistic  Median  Is the score that divides the group in half (with 50% scoring below and 50% scoring above the median)  It is an appropriate indicator of central tendency when scores are on an ordinal scale because it takes into account only the rank order of the scores. It is also useful with interval and ratio scale variables  Mode  Is the most frequent score  It is an appropriate indicator of central tendency if a nominal scale is used. The mode does not use the actual values on the scale, but simply indicates the most frequently occurring value  Median and mode are better indicators of central tendency when few unusual scores bias the mean 2. Variability  A measure of variability is a number that characterizes the amount of spread in a distribution of scores.  Standard deviation (SD) indicates the average deviation of scores from the mean. SD is appropriate only for interval and ratio scale variables  The SD of a set of scores is small when most people have similar scores close to the mean and becomes larger as more people have scores that lie further from the mean value.  SD is the square root of variance  Variance is the measure of variability of scores about the mean  Range is the difference between the highest score and the lowest score o These two numbers (central tendency and variability) summarize the information contained in a frequency distribution Graphing Relationships  A common way to graph relationships between variables is to use a bar graph or a line graph o Bar graphs are used when the values on the x axis are nominal categories o Line graphs are used when the values on the x axis are numeric Correlation Coefficients: Describing the Strength of Relationships  A correlation coefficient is a statistic that describes how strongly variables are related
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