Jargon Basic Concepts Howell Statistical Methods for Psychology

Jargon & Basic Concepts Howell Statistical Methods for Psychology

Questions • Define and illustrate: – Population, Sample – Parameter, Statistic – Descriptive, inferential statistics – Random selection (sampling), assignment – Internal, External validity – Discrete, continuous variables – Scale types (nominal, ordinal, interval, ratio)

Population vs. Sample • Population – collection of all the objects of interest to researcher (you). – College students, students at USF • Sample – subset of objects from the population – Want a representative sample – Samples are relatively practical – Random samples have good properties – One person’s sample is another’s population

Parameter vs. Statistic • Parameter – numerical summary of population – E. g. , mean, standard deviation • Statistic – numerical summary of sample – E. g. , mean, standard deviation • Typically we compute statistics and estimate parameters using statistics.

Descriptive vs. Inferential • Descriptive statistics describe a sample – How tall are these students? • Inferential statistics use sample statistics to make decisions about populations. – Is one method of instruction better than another?

Random Select & Assign • Random selection is a process of picking a sample from a population so that each element has the same probability of being sampled. – E. g. , lottery, every 3 rd name from a list (this is actually a systematic sample but it’s good) • Random assignment is assignment to treatment so that each element has an equal probability of being assigned to each treatment. – E. g. , lottery, every other name, etc. • Both are typically accomplished by lists (aka frames) and computer generated numbers (e. g. , SAS PROC PLAN)

Internal, External Validity • Internal validity - quality of inferences about the study itself. Random assignment, history, maturation, etc. • External validity – quality of inferences from the study to the larger domain of interest. Representative sample of participants, task relevance, behavioral consequents, etc. Aka generalizability of the results (but not generalizability study).

Variable & Distribution • Variable vs. constant – Attribute either varies across objects or not • Distribution: Collection of data • Distribution: Array of scores – Height – Beck Depression Index – Rat bar press – Wonderlic

Discrete vs. Continuous • Math – Integer vs. real numbers • Data – Categorical vs. continuous (many valued, ordered) • Examples Political party, job satisfaction, response time, country of origin

Scale types • Nominal, ordinal, interval, ratio • Nominal – categories. No ordering; mean has no connection to attributes • Ordinal – rank order only • Interval – rank order plus equal interval. ratio of differences has meaning • Ratio – rank order, equal intervals, rational zero point. Ratio of numbers has meaning.

Scale Types: Footrace review Nominal Ordinal Interval Ratio ID number Rank order Time of day Elapsed of finish time from start 043 1 10: 57 a. m. 4 min 011 2 10. 59 a. m. 6 min 136 3 11: 01 a. m. 8 min 112 4 11: 02 a. m. 9 min 086 5 11: 04 a. m. 11 min

Review Find a partner to work on this exercise. Suppose you want to know whether one brand of tennis shoe is better than another. You have about $10 K from a grant to study this. Describe a study you might conduct to find out. What might be your population, sample, independent and dependent variables? What statistics might you want to compute? Never mind the actual statistical test at this point. What data would you gather? What might a critic say about the internal and external validity of your study? What scale types are your IV and DV?