
measurement
observable characteristics of our variables

triangulations
multiple approaches in one research study; means to enhance the quality of the work done

methodologically
using both quantitative and qualitative methods

data
instead of using methods, bring in data from different instituations or places

researcher triangulation
have multiple researchers collect data

theoretical triangulation
bring in different theories and triangulate betweent two to three of them

Variable measurement: nominal
simply asking a question that puts a variable into a category.

Nominal characteristics:
 must be mutually exclusive,
 must be equivalent
 must be exhaustive > you leave no possible responses out

Ordinal characteristics
 must meet all the nominal characteristics, but is also rank order
 anytime you have to rank your sources, such as grades or team sports

Interval characteristics
 equal distances between two points on the scale
 we will know how much someone likes something
 zero rating: its a point on the scale, doesnt mean it doesnt exist

Likert Scales
 scale must be an interval
 low numbers are disagree, higher numbers are agreement (15)
 likert type scale uses any modification such as 17

semantic differential scales
polar opposites on both ends of the scale

ratio measurement scales
zero does not mean the variable does not exist

tunnel method
same types of questions throughout

funnel method
start with broader questions, then narrow them down

inverted funnel method
specific to broad

4 question order effects
 consistency: lying about something up front means you will probably lie the whole way through
 fatigue: questionnaire is too long
 redundancy: similar questions posed in different ways annoy the reader and they can start to check boxes
 response set: clicking one question the whole way through



internal validiy
 draw accurate conclusions from my research
 we want a high amount of internal validity

external validity
how generalizable are my findings, as in, how much can i apply what i found to other people

if you are valid you must be
reliable as well. you are not necessarily valid if you are reliable

measurement reliability
consistency of our measure and establishes high internal validity

Three ways to have measurement reliablity
 test/retest method: comparing when tests are taken twice
 alternative forms: give one group of people your measure and also give them a measure that is testing about a similar concept
 split half: compare each half of the quetions to each other

internal consistency method
 best method to have measurement reliability
 calculates all the possible split halves, all the possible outcomes to give the best one
 Chronbachs Alpha gives this to us, we want higher than .7

Threats to internal validity
 history effect: something external to the study affects the participants
 sleeper effect: effects of the study took longer than expected and you didn't wait long enough to observe them
 sensitization: give a group a measure and ask them to take the same measure at a later time; didnt give them long enough to forget their answers
 data analysis: certain procedures and statistical analysis should be used in certain instances

more threats to internal validity
 participants:
 must have good incentive
 Hawthorne effect: know they are being studied
 selection: who are the people being studied
 statistical regression to the mean: need a larger group of people if your average is unusually high
 mortatlity/attritition: people drop out of the study
 maturation: internal changes that are going on in the participant

researcher threats to internal validity:
 combat threats to ourselves
 blind procedure: participants do not know what group they are in
 double blind: neither participants or researcher know what group they are in

external validity:
ecological validity: does the research describe what is accurately happening in real life

replication:
 exact: repeat study in exact same way
 partial: keeps some of the study the same, but modifies others
 conceptual: keeps the concept of the study the same but uses entirely new procedures

what is a sample
a subgroup of people collected from both a population and target population that actually participate in the study

three problems with samples
 size: contingent on statistical distribution need a minimum of thirty people
 bias: systematically exculded some people that could have been useful to the study
 representativeness: we want our sample to accurately represent our population

sampling frame
list of all possible participants

sampling unit
each person on the roster

sampling error:
the extent to which my sampling deviates from my population

confidence level
how confident i am that my sample is representative of my population > want to be 95% or higher

confidence interval:
plus or minus range that i believe my sample will fall between

IRB guiding principle
the rewards of doing my research do not outweigh the costs


conceptualization:
the dictionary definition of what you are going to stuyd

operationalizaton:
how are you going to measure your variables

Data collection
what method are you going to use

data analysis:
what statistical tests are you going to use; qualitative or quantitative

reconceptualization
discussion about how does it fit in with the theory, is more research needed, etc

Epistemology
 Pos: researcher and work are seperate from one another
 Nat: your perspective affects your research

Axiology:
 pos: research should be value free, unbiased
 nat: how you view the world is important to your research

Methodological
 pos: deduction, reason down to a conclusion cause and effect, quantitative methods
 nat: induction specific to general, goal is a wholistic understanding at human behavior

Rhetorical assumption
 Pos: standardized, in the third person
 Nat: first person, more story telling type way

Research Question and the three types
 tends to be less defined unsure of what to look for or find
 descriptive: describe something or something that there is no information about
 tests of association: purpose of there is a relationship between concepts being studied
 Tests of difference: looking for differences between groups

Hypothesis and two types
 hypothesis is a statement
 tests of association: purposing a relationship
 tests of difference: will state a difference between variables

Two tailed and one tailed questions
 two tailed: does not predict a direction
 one tailed: predicts a direction between the variables

independent variable
influences change in the dependent variable

dependent variable
is the variable being changed

recursive cuasal model:
 one way street: one cause > one effect
 ie: smoking cigarettes cuases cancer, and not the other way around

nonrecursive cuasal models
two way street, reciprocal cause and effect go both ways

noncausal relationship:
variables are related but changes in the one do not cause changes in the other

