Psychological Statistics Lecture Notes PDF

Wesleyan University - Philippines Psychological Statistics INSTRUCTOR: DANA SOLIVEN  Today's Agenda SAMPLING TECHNIQUES PROBABILITY AND NON-PROBABILITY SAMPLING DATA COLLECTION  DIFFERENTIATE SAMPLE FROM POPULATION Learning DISTINGUISH PROBABILITY WITH NON-...

Wesleyan University - Philippines Psychological Statistics INSTRUCTOR: DANA SOLIVEN  Today's Agenda SAMPLING TECHNIQUES PROBABILITY AND NON-PROBABILITY SAMPLING DATA COLLECTION  DIFFERENTIATE SAMPLE FROM POPULATION Learning DISTINGUISH PROBABILITY WITH NON- Objectives PROBABILITY SAMPLING IDENTIFY VARIOUS DATA COLLECTION TECHNIQUES Population THE COLLECTION OF ALL PEOPLE WHO HAVE SOME CHARACTERISTIC IN COMMON Sample SMALL SUBSET OF A POPULATION Sampling Techniques Sampling is the process of selecting a sample for the purpose of making conclusions about the population. To make conclusions valid, it is important that the sample chosen is representative and sufficient Representativeness means that the sample should be very much the same (in terms of characteristics) as the population the absence of prejudice, bias or undue preference would ensure that whatever conclusions will be made based on the sample will also applicable to the population Sufficiency means that the sample consists of an adequate number of entities (the sample is large enough) to confidently claim that it can stand for the population  Probability Non-Probability Sampling Sampling (Non-Random Sampling) (Random Sampling) samples are chosen in some specific each member of the population has an manner by the researcher, making it equal chance or possibility of being selected relatively biased to participate in a study  Probability Sampling Simple Random Sampling - sample is chosen based on pure chance ex: Lottery / Draw lots / Fishbowl method Systematic Random Sampling - sample is chosen based on the nth selection system ex: only including every 7th student in the class list Stratified Random Sampling - technique that is ideal for populations made up of distinctly different groups (or strata) because it maintains the proportion among the groups ex: the population of 2nd year WU-P Psychology students is dominated by female students, in order to yield a more accurate sample so as to include the male strata, the proportion of the population is reapplied to the sample (if 90% of the students are female and 10% are male and the sample is supposed to be made up of 40 students, Four male students (10% of 40) will be included in the sample, so as to more reliably represent the population  Probability Sampling Cluster Sampling - commonly used when the population is naturally composed of strata or clusters (small subsets that relatively mimics the characteristic of the whole population) ex: to find out the views of Filipinos toward the RA 11479, the researcher would divide the country into geographical clusters (Region 1 - BARMM), a certain number of regions will be randomly selected from which a sample would be taken  Non-Probability Sampling Convenience Sampling - the sample is selected based on their availability ex: requesting friends to be part of your experiment / study Purposive Sampling- respondents are chosen based on the specific description imposed by the research topic ex: hard-of-hearing individuals are the sole respondents selected for a study regarding the role of lip reading in communicating while wearing face masks Quota Sampling- non-probability version of Stratified Random Sampling, subsets are identified and from those groups, the researcher will be the one to define the quota or number of respondents for each group i.e. the researcher identifies the exchange student stratum within the student body but does not check for the exact proportion, instead arbitrarily assigning a number of exchange students for them to reach  Non-Probability Sampling Snowball or Referral Sampling - when the topic of interest is rare or finding possible members of the population is difficult due to certain circumstances, the researchers may ask the respondents to direct them or refer them to other members of the population if they know any ex: when looking for respondents for a study involving men who have experienced domestic violence, the researcher may opt to ask among the respondents (who have already been interviewed) if they know of anyone else who have had similar experiences  Data Collection Two basic sources of data: Primary Data: Secondary Data: information gathered by the information obtained from researchers themselves published and unpublished materials  Methods in Collecting Primary Data Observation Survey Psychometrics identifying the behaviors, providing a questionnaire administering tests of responses, stuations, or to the sample and collating intelligence, aptitude or other objects of study and noting psychological aspect that their responses to the items the frequency and manner of can be done individually or by its occurrence groups a relatively large number of data can be gathered may be done in the data itself will be using this method Controlled Environment procured by interpreting the (Laboratory Setting) test-takers' responses in the Natural Setting test  Methods in Collecting Primary Data Experiment Interview the psychologist manipulates Face-to-face question and answer one variable (Independent process between the researcher (interviewer) and the respondent variable) while holding all the (interviewee) other variables constant by noting how the manipulated variable affects the response variable (Dependent variable), the researcher can establish the presence or absence of a Causal Relationship Reference Howell, D. C. (2010). Statistical methods for psychology. Belmont, CA: Thomson Wadsworth.