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Lecture 6

Lecture 6.docx

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Connie Boudens

Experiments Prof’s Speech – Purple Types of Questionnaires - Interviewer-administered o Face-to-face o Telephone - Self-administered o Mail/email, internet Sampling – important from the practical/conceptual standpoint - Surveys are trying to get a representative sample (so that it can be generalized to the greater population) - A population is the entire pool from which a sample is drawn o A population in this sense is not everyone who lives in a particular place o Sampling unit – usually a person  Can be an organization, group, item, group of items, etc. o Sampling frame – optimally, will be the same as the population Key distinction Probability and non-probability samples - Probability samples o There is an equal probability of each person being drawn (ex. Opinion polls) o Should use probability samples if you want to get better results o Random; everyone in the population has an equal chance at selection  Note: scientific random ≠ colloquial random  „Random‟ in science still has strategy, it is not haphazard, still planful, but there is no set system in regards to how the selection will take place o Type: stratified samples  Needs a certain number of people from each group in a population o Type: cluster samples  Identifying clusters in an area and drawing information from the cluster - Non-probability samples o Opposite of probability samples o Not everyone has an equal possibility of being in the sample o Non-probability samples are used because they are cheap and easy, but it is difficult to extend findings from these samples to the greater population o Type: convenience samples  Ask passersby in a mall (for example) to do an experiment o Type: snowball samples  Participants are asked to „tell a friend‟ and those friends „tell a friend‟ too  Not everyone has a chance to participate in these samples o Type: purposive sampling  Looking for people knowledgeable of a certain field In terms of the scientific approach – we are still in the testing phase Some relations between variables are just a relation, they are not necessarily – one variable causes the other variable (Example: relation between those who watch soap operas and eating disorders – soap operas cannot cause eating disorders) Problems with establishing causality in correlational research  Direction of influence problem o Ex: class attendance and good grades – is it that those with greater attendance get the higher grades? Or that those with higher grades come to class more? o X  Y or X Y  Third variable problem o Another variable affects the result; there‟s another variable influencing both variables o Ex: relation between ice cream sales and number of drownings  There may seem to be some relation, but temperature is really affecting both variables o X – Y, but Z  X and Z  Y Three things needed to establish causality  Temporal order must be correct o The cause has to come before the outcome  Variables have to covary  No other variable is causing the outcome o Experiments help in making sure that no other variable is causing the outcome - Experiments can help by: o Holding extraneous variables constant  When you hold them constant, you may still have some influence, but the influence will be the same for all participants  If you can‟t hold them constant, use random assignment  Random assignment is when participants are assigned to one condition or another  Independent variable is always presented first, and is the causal factor; the independent variable causes the dependent variable Extraneous vs confounding variable  Confounding variables are related to the independent variable  Distinctions o Extraneous variables  Anything other than the independent variable that could affect the dependent variable o Confounding variables  Subset of extraneous variables  They are related to the independent variable How experiments allow you to do this (see notes above)  Hold all variables constant  Use random assignment  Present i.v. first  Present i.v. carefully and consistently, measure the d.v. rigorously Basic experimental design(s)  One i.v. two levels o One independent variable that has two levels, which are usually:  Experimental group  Sometimes called the treatment group  Encounter the experimental stimulus  The group where something happens  Control group  Doesn‟t receive the experimental stimulus  But is the same as the experimental group in all other ways  In drug experiments, this group will get placebos  Sometimes the two groups are just two groups that you are contrasting with each other o With the same time between each stage:  Experimental group  administered IV  measure DV  Control group   measure DV  One d.v. o One IV, one DV Pretest- posttest design  Make sure groups are actually equivalent  Identify people with high/low pre-existing characteristics  In case of mortality  Measure change in each individual  Pretest measure – ensure that there are no pre-existing differences between the groups involved, and are done before anything is done/administered to the groups o Pretest measures are extra check to make sure that the groups are equivalent o Pret
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