Introduction to Statistics & Research Design PDF

Introduction to Statistics & Research Design 2 BRANCHES OF STATISTICS | HOW TO TRANSFORM OBSERVATIONS INTO VARIABLES | VARIABLES AND RESEARCH | INTRODUCTION TO HYPOTHESIS TESTING 2 BRANCHES OF STATISTICS  DESCRIPTIVE STATISTICS  Organize, summarize, and communicate a group of numerical obs...

Introduction to Statistics & Research Design 2 BRANCHES OF STATISTICS | HOW TO TRANSFORM OBSERVATIONS INTO VARIABLES | VARIABLES AND RESEARCH | INTRODUCTION TO HYPOTHESIS TESTING 2 BRANCHES OF STATISTICS  DESCRIPTIVE STATISTICS  Organize, summarize, and communicate a group of numerical observations  Describes large number of data in a single number or in just as few numbers.  Example:  These averages are descriptive statistics because they describe the weights of many people in just one number. A single number reporting the average is far more useful than a long list of weights of every person studied by the CDC.  INFERENTIAL STATISTICS  Use sample data to make general estimates about the larger population  Inferential statistics infer, or make an intelligent guess about the population  Example:  The CDC made inferences about weight even though it did not actually weigh everyone in the United Staties. Instead, the CDC studied smaller, representative group of US citizens to make an intelligent guess about the entire population. SAMPLE VS POPULATION  SAMPLE  A set of observations drawn from the population if interest.  POPULATION  Includes all possible observations about which we’d like to know something THE RESEARCH PROCESS (1)Initial Observation : finding something that needs explaining “ Why are there many problematic personalities in reality shows like Big Brother?” (2)Generating theories and testing them Theory: (1)People with narcissistic personality disorder are more likely to audition for Big Brother than those without. (2) Producers will more likely select people with narcissistic personality to be contestants than those with less extreme personalities. Hypothesis Testing (3) Data Collection to Test theory  When we collect data we need to decide on two things: (1) what to measure (2) how to measure it. VARIABLES Independent, Dependent, Confounding  An independent variable has at least two levels that we either manipulate or observe to determine its effects on the dependent variable.  The dependent variable is the outcome variable that we hypothesize to be related to, or caused by, changes in the independent variable.  A confounding variable is any variable that systematically varies with the independent variable so that we cannot logically determine which variable is at work. In experimental work the cause, or independent variable, is a predictor, and the effect, or dependent variable, is simply an outcome.  To Illustrate If we are studying whether gender predicts one’s attitude about politics Independent Variable independent variable is gender with two levels: female and male. Dependent Variable Attitude about politics Confounding Variable Family influence, education, socio-economic status etc. HOW TO TRANSFORM OBSERVATIONS INTO VARIABLES  VARIABLES  Observations of physical, attitudinal, and behavioral characteristics that can on different values.  Behavioral scientists often study abstract variables such as motivation, self- esteem and attitudes. Discrete Observations Continuous Variables Nominal Ordinal Interval Ratio Variables Variables Variables Variables DISCRETE OBSERVATIONS  Can take only specific values; no other values can exist between these numbers  Example: If we measure the number of times a study participants gets up early in a particular week, the only possible values would be whole numbers. It is reasonable to assume that each participant could get up early 0 to 7 times in any given week, but not 1.6 or 5.92 times. (1) NOMINAL VARIABLES  Use for observation that have categories or names as their values. (2) ORDINAL VARIABLES  Are observations that have rankings (i.e. 1st, 2nd , 3rd, …) as their values CONTINUOUS VARIABLES  Can take a full range of values; an infinite number of potential values exists.  Example: One person might complete a task in 12.389 seconds. Someone else might complete it in 14.740 seconds. The possible values are continuous, limited only by the number of decimal places we choose to use. (1) INTERVAL VARIABLES  Are used for observations that have numbers as their values; the distance (or interval) between pairs of consecutive number is assume to be equal.  Examples: temperature, IQ, SAT/ACT test scores (2) RATIO VARIABLES  Are variables that meet the criteria for interval variables but also have meaningful zero points.  Examples: weight, height MEASUREMENT ERROR  There will often be a discrepancy between the numbers we use to represent the thing we’re measuring and the actual value of the thing we’re measuring (i.e. the value we would get if we could measure it directly). This discrepancy is known as measurement error.  Example: Imagine that you know as an absolute truth that you weight 83 kg. One day you step on the bathroom scales and it says 80 kg. There is a difference of 3 kg between your actual weight and the weight given by your measurement tool (the scales): there is a measurement error of 3 kg.  Although properly calibrated bathroom scales should produce only very small measurement errors (despite what we might want to believe when it says we have gained 3 kg), self-report measures do produce measurement error because factors other than the one you’re trying to measure will influence how people respond to our measures. RELIABILITY AND VALIDITY  One way to try to ensure that measurement error is kept to a minimum is to determine properties of the measure that give us confidence that it is doing its job properly.  Validity refers to whether an instrument measures what it was designed to measure.  Reliability refers to the consistency of a measure. Validity is a necessary but not sufficient condition of a measure. A second consideration is reliability, which is the ability of the measure to produce the same results under the same conditions. To be valid the instrument must first be reliable. INTRODUCTION TO HYPOTHESIS TESTING  How do we measure the phenomena/constructs we want to study?  Social scientists use research to test their ideas through a specific statistics-based process called hypothesis testing. Hypothesis testing is the process of drawing conclusions about whether a particular relation between variables is supported by the evidence.  Typically, when we test a hypothesis, we examine data from a sample to draw conclusions about a population. There are many ways to conduct research.  An operational definition specifies the operations or procedures used to measure or manipulate a variable.  Example: How do we operationalize anxiety?  Try this! Data Collection Research Methods  Correlational Research Methods vs Experimental Research Methods The main distinction between what we could call correlational or cross-sectional research (where we observe what naturally goes on in the world without directly interfering with it) and experimental research (where we manipulate one variable to see its effect on another) is that experimentation involves the direct manipulation of variables.  Is there a correlation between aggression and playing of video games?  A researcher conducts an experiment to see whether the level of histamine (IV) has an effect on a person’s drowsiness/sleepiness(DV). The hallmark of experimental research is random assignment. With random assignment, every participant in the study has an equal chance of being assigned to any of the groups, or experimental conditions, in the study. In a between-groups research design, participants experience one, and only one, level of the independent variable. In a within-groups research design, the different levels of the independent variable are experienced by all participants in the study; also called a repeated-measures design.