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# Psych Statistics Psych 221

SELU

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This 9 page Class Notes was uploaded by Alexander Chirieleison on Wednesday August 24, 2016. The Class Notes belongs to Psych 221 at Southeastern Louisiana University taught by Susan Coats in Fall 2016. Since its upload, it has received 12 views. For similar materials see Psychological Statistics in Psychology (PSYC) at Southeastern Louisiana University.

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Date Created: 08/24/16

Introduction to Psych Statistics 08/30/2016 ▯ 1st step in data analysis is to clean the data ▯ ▯ Best methodd for test taking: do the easiest items first (memorized material) and then move on to the calculations. ▯ ▯ Statistics measures error rates ▯ - There is a large error rate in social sciences like psychology ▯ ▯ - helps people understand data that empirical research provides ▯ ▯ - we need statistics for both experimental and non-experimental studies ▯ ▯ Two kinds of statistics: descriptive and Inferential ▯ ▯ - Descriptive statistics: describe data ▯ ▯ - more important than inferential statistics ▯ ▯ - Inferential statistics: used to make casual inferences ▯ ▯ - You must have multiple, corroborated replications to know what is going on ▯ ▯ Independent variable = manipulated variable (cause) ▯ ▯ - sometimes called a predictor ▯ ▯ Dependent variable = measured variable (effect) ▯ ▯ - sometimes called a predicted variable ▯ ▯ Sample v. Population ▯ ▯ Sample: the data points that a researcher has access to out of a group of interest ▯ ▯ Population: the total group of interest ▯ ▯ Representative sample ▯ ▯ - the sample data collected must be used to generalize to a population ▯ ▯ - the most basic way to get a representative sample so that one can generalize to the population is to take a pure random sample ▯ ▯ - pure random samples are the least efficient method ▯ ▯ - pure random sampling requires a much larger sample size ▯ ▯ Sampling with/out replacement is more common in psychology ▯ ▯ - Sampling without replacement: participants selected randomly for a study cannot be selected again for the same study ▯ ▯ - Its more efficient to sample with replacement; a person can be randomly selected more than once for a study ▯ ▯ - Psychologist do not use sampling with replacement because it is more difficult to account for the margin of error ▯ ▯ - Psychologists use sampling without replacement because the data points are independent of one another ▯ ▯ The statistics assume the data points are independent of the dependent variable; if psychologists used sampling with replacement they would have to account for the dependency in the data ▯ ▯ Sampling rate: the proportion of subjects from a given population ▯ ▯ Example: selecting 10 subjects from a population of 100 would give us a sampling rate of 0.1 ▯ ▯ Psychologists have to choose an adequate sample size for their study to have any external validity ▯ ▯ Variables v. Constants ▯ ▯ - variables change ▯ ▯ - constants do not change ▯ ▯ Fixed v. Random variables ▯ ▯ Random variable: a variable that is free to change (e.g. personality, height) ▯ ▯ Fixed variable: a variable that is measured without error ▯ ▯ Side Note: some of the statistics we have assume that the independent/predictor variable is fixed, when in fact the variable is not fixed ▯ ▯ Side Note 2: the dependent/predicted variable is typically a random variable ▯ ▯ Random selection v. Random Assignment ▯ ▯ Random selection, to create a representative sample, should not be confused with random assignment ▯ ▯ Random selection: researchers randomly pick participants for their study from the population ▯ ▯ Random assignment: researchers randomly put participants in different conditions of the study ▯ ▯ Random assignment reduces the error rate by balancing the source of error between groups in a study ▯ ▯ Some studies are constrained to correlation research, due to ethical constraints, and therefore cannot take representative samples. ▯ ▯ Matching ▯ ▯ - Researchers want to make the experimental and control group as equivalent as possible so that any differences that emerge once the treatment is introduced, can be contributed to the treatment. ▯ ▯ - The aim is to equally distribute, as much as possible, the effect of extraneous variables between the experimental and control group. ▯ ▯ - There are some extraneous variables that researchers don’t know about, so they use random assignment to balance sources of error that are unknown ▯ ▯ The best form of matching = Repeated measures design ▯ ▯ Each participant appears in each condition of the experiment ▯ ▯ - In a repeated measures design, the participants are perfectly matched ▯ ▯ Discrete variables v. continous variables ▯ ▯ Discrete variables contain nothing except the actual value and have no intermediate ▯ ▯ e.g. whole number, sex ▯ ▯ Continuous variables - there is always an intermediate ▯ ▯ e.g. personality, intelligence ▯ ▯ Even something which is considered a continuous variable can be measured as a discrete entity. ▯ ▯ e.g. IQ scores ▯ ▯ Conceptually there is a continuum between a score of 100 and a score of 101, but they are measured as discrete numbers. ▯ ▯ p21 difference between discrete and continuous variables ▯ ▯ Levels of measurement ▯ ▯ Nominal: simple categorization; this is the weakest level of measurement ▯ ▯ Ordinal: categorization + greater/lesser values ▯ ▯ Examples: IQ, social skills, introversion ▯ ▯ Interval: categorization + greater/less + equal intervals ▯ ▯ example: the difference between a score of 1 and 2 is equal to the difference between a score of 2 and 3 ▯ ▯ Ratio scale: an equal interval scale with an absolute zero ▯ ▯ Do not exist in psychology and are rare in science ▯ ▯ - Degrees Kelvin ▯ ▯ Sometimes psychologists treat ordinal level data as thought it was interval data ▯ ▯ Most statistics assume an interval level of measure for the dependent variable ▯ ▯ Example: IQ is an ordinal measure but is treated as an interval measure ▯ ▯ IQ: floor and ceiling effect ▯ ▯ Floor effect: the variance in intelligence disappears at the bottom end of the distribution ▯ ▯ example: difference between a IQ of 50 and an IQ of 51 is not a meaningful difference ▯ ▯ Ceiling effect: the variance in intelligence disappears at the top end of the distribution ▯ ▯ example: the difference between a 120 and 150 IQ involves so few test items that the difference becomes less meaningful ▯ ▯ Example: the top end of age distribution v. the bottom end of age distribution from a developmental psychology perspective ▯ ▯ - Development across multiple variables is non-linear ▯ ▯ Example: the intervals between 1 and 6 years of age are not equal to the intervals between 50 and 55 years of age ▯ ▯ Two basic dependent variables in all of psychology- ▯ ▯ - Number correct/wrong and reaction time ▯ ▯ - There is a skewed distribution with reaction times to certain stimuli ▯ ▯ - a few participants will be very fast, most participants will be around the mean, and a few people who aren’t doing the task ▯ ▯ Beck Depression Inventory scores ▯ ▯ - uses interval measures on a 4point -likert scale ▯ ▯ - each individual item is rated on a scale of 0 to 3 ▯ ▯ - to score a interval measure test you add up the ordinal ratings and get a total ▯ ▯ - remember the first step in data analysis is clean the data ▯ ▯ - look for outliers in the data that don’t make sense ▯ ▯

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