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Population & Samples

by: Amy Turk

Population & Samples Psyc-21621

Amy Turk

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Information covering population & samples.
Quantitative Methods Psych I
Dr. Gordon
Class Notes
Psychology, Stats, Statistics, population, sample, variable, data, datum, parameter, Descriptive, inferential, correlational, experimental, manipulation, control, participant, Environment, random assignment, random sampling, matching, independent, Dependent, pre-post, study, quasi-independent, operational, Discrete, Continuous, nominal, ordinal, interval, ratio
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This 4 page Class Notes was uploaded by Amy Turk on Wednesday April 27, 2016. The Class Notes belongs to Psyc-21621 at Kent State University taught by Dr. Gordon in Spring 2016. Since its upload, it has received 6 views. For similar materials see Quantitative Methods Psych I in Psychlogy at Kent State University.


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Date Created: 04/27/16
PSYCH STATISTICS CHAPTER ONE Populations and Samples ● statistics = mathematical procedures for organizing, summarizing, and interpreting info ● population = set of all the individuals of interest in a study ○ can be pretty big ● sample = set of people selected from a population, usually intended to represent the population ● variable = a characteristic or condition that changes or has different values for different individuals ○ ex. height, weight, gender, personality ● data = measurements or observations ● data set = collection of measurements or observations ● datum = single measurement of observation ○ often called a score ● parameter = value, usually numeric, that describes a population ○ usually derived from measurements of the people in the population ● statistic = value, usually numeric, that describes a sample ○ usually derived from measurements of the people in the sample ● every population parameter has a corresponding sample statistic ● statistical procedures have 2 categories: ○ descriptive statistics = take raw scores and organizes or summarizes them in a form that is more manageable ■ usually a table or a graph ■ computing an average ○ inferential statistics = use sample data to make general statements about a population ● a sample provides only limited info about the population ○ doesn’t give a perfectly accurate picture of the whole thing = sampling error ○ because the characteristics of each sample depend on the specific people in the sample, statistics vary from one sample to another Data Structures and Research Methods ● most research is intended to examine relationships between two or more variables ● the correlational method ○ observe the two variables as they exist naturally for a set of people ■ then studied to see if there’s a relationship between them ○ results do not provide an explanation for the relationship ○ ex. wake-up time and academic performance 1 ● experimental method = comparison of two or more groups of variables ○ uses one of the variables to define the groups and the second variable to obtain scores for each group ○ ex. one group of elementary kids is shown a 30 minute action video with violence and another is shown a 30 minute comedy with no violence. Both groups are observed on the playground for acts of aggression ○ demonstrates a cause and effect relationship between two variables ■ attempts to show that changing the value of one variable causes changes to occur in the 2nd variable ● manipulation = researcher manipulates one variable by changing its value from one level to another. A 2nd variable is measured to determine whether the manipulation caused changes to occur. ● control = researcher must have control over the research situation to ensure that other variables don’t influence the relationship being examined ○ 2 general categories of variables ■ participant variables = characteristics that vary from one individual to another ● age, gender, intelligence ● whenever an experiment compares two types of participants, researchers must ensure that participant variables do not differ from one group to another ○ can’t have mostly girls in one and mostly boys in another ● whenever a research study allows more than one explanation for the results, the study is said to be confounded because it’s impossible to reach one conclusion ■ environment variables = characteristics of the environment - lighting, time of day, weather conditions ● people in treatment A must be tested in the same environment as people in treatment B ○ techniques to control other variables ■ random assignment = each person has an equal chance of being assigned to each of the treatment conditions ● distributes the participant characteristics evenly ● can also be used to control environmental variables ■ matching = ensures equivalent groups of environments 2 ● ex. 60% of females and 40% of males ■ holding them constant = ex. all ten year olds ○ so in experimental method, one variable is manipulated while another is observed and measured ■ attempts to control all other variables to avoid them influencing the results ○ independent variable = manipulated by researcher ■ usually consists of two treatment conditions to which subjects are exposed ■ always consists of at least two values: treatment vs. no treatment ○ dependent variable = observed to assess the effect of treatment ○ people in a control condition do not receive the experimental treatment ■ provides a baseline for comparison with the experimental condition ○ experimental condition = when people do receive experimental treatment ● nonexperimental methods = not true experiments but still examine the relationship between variables by comparing groups of scores ○ nonequivalent group study: the researcher has no ability to control which participants go into which group ■ compares pre existing groups… the researcher can’t control the assignment of participants to groups and can’t ensure equivalent groups ■ not a true experiment ○ pre-post study: two groups of scores are obtained by measuring the same variable twice for each participant, one before and one after ■ ex. depression scores before and after therapy ■ the researcher has no control over the passage of time or other variables relating to it ● not a true experiment ○ quasi-independent variable = in a nonexperimental study, the independent variable that is used to create the different groups of scores Variables & Measurements ● variables like intelligence, anxiety, and hunger are called constructs ○ because they cannot be directly observed, they are hypothetical constructs ○ it is possible to observe and measure behaviors that are representations of constructs 3 ■ ex. we can see examples of intelligent behavior ● operational definition = set of operations for measuring an external behavior and uses the resulting measurements as a definition and a measurement of a hypothetical construct ○ describes a set of operations for measuring a construct ○ defines the construct in terms of the resulting measurements ○ ex. intelligence is measure and defined by performance on an IQ test ● discrete variables ● continuous variables ○ very rare to find identical measurements for 2 different people ● nominal scale = classifying people into categories that have different names but are not related to each other in any systematic way ○ does not specify the direction or size of the difference ○ not quantitative values, but are occasionally represented by numbers ■ ex. room #5 ● ordinal scale = categories have different names and are organized in terms of size or magnitude ○ ranks ○ ex. reading levels ○ often used to measure variables for which it is difficult to assign numerical scores ■ ex. people rank food preferences but have a hard time expressing exactly how much more they prefer one food over the other ● interval scale = ordered categories that are all intervals of exactly the same size ○ a zero is assigned to a particular location as a reference = doesn’t indicate a total absence of the variable being measured ● ratio scale = interval scale with additional feature of an absolute zero point ○ rations of numbers do reflect ratios of magnitude ○ we can measure the absolute amount of the variable = we can measure the distance from zero ○ we can measure the direction and size of the difference between two measurements ○ ex. height & weight & number of errors on a test 4


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