Chap 1 Flashcards
5 Characteristics of Solving Problems
Systematic, Logical, Empirical, Reductive, Replicable
Systematic
Identification, Labeling, Research of Testing, evaluation
Logical
Examination
Empirical
collection of data to base directions
reductive
processing events and using them to establish relationships
Replicable
repeating research or building research of previous results
applied research
theory driven, lab
moderate research
theory driven using revelent movements, similar to real world
basic research
immediate solutions, real world
scientific method of problem solving
defining and delaminating the problem, forming hypothesis, gather data, analyze data, interrupt data
unscientific methods of problem solving
tenacity, intuition, authority, rationalistic, empirical
tenacity
beliefs regarding lack of supporting evidence
intuition
common sense or self evident
authority
accept or reject the information
rationalistic
reasoining
empirical mehtod
based on objective observations
Parts of a Thesis
Proposal of research plan, completing thesis
proposal of research plan
intro, review, method
completing thesis
result
kinesiology
study of the bodys movement
identifying a research problem
broad focus, reading review paper, read research lit,
criteria in selecting a research problem
researchable, interest, unity, worthwhile, feasible, timely, look without prejudice, prepared in tech to address problem
six steps in the lit search
write the problem statement, consult secondary sources, determine descriptors, find primary sources, read and record lit, write lit review
developing the research hypothesis
research hypothesis, educated guess
null hypothesis
no significant differences between groups
possible problem with research problem
research problem is identifying area that isn’t well understood in lit
dependent variable
what we want to know effect
independent variable
manipulated by researcher cause
extraneous variable, confounding variable
acts as independent variable but it is not
what is in a well written method section
someone can duplicate your methods, includes participants, instrument, procedures, design, analysis
2 principles of planning
less is more and simple is better
scientific misconduct
fabrication, falsification, plagiarism in proposing performing reviewing research or in reporting research results.
institutional review board(irb)
reviews studies from human studies, protection
animal subject committee(iacuc)
protects animals
informed consent document
approval of consent
types of statistics
descriptive, correlation, differences among groups
descriptive statistics
describing what we see
correlation statistics
pearson correlation(r) measures linear association between two variable
differences among groups
differences
ways to select a sample
random sampling, stratified random sample, systematic sampling, random assignment
gold standard to sampling
simple random sample
measures of central tendency scores
mean, median, mode
variability of central tendency scores
standard deviation, range of scores
X bar refers to as
sample mum
m refers to as
population mean
standard deviation is what letter
s
variance is what letter
s^2
categories of statistical tests
parametric, nonparametric, normal curve
parametric data is continuious
ratio, interval
non parametric data non continuous
nominal=name, ordinal=order
normal curve
skewness, kurtosis
ratio parametric data always begins at
0
interval parametric data
doesn’t always begin at 0
skewness distribution is
on one side or the other distribution
kurtosis
doesn’t seem normal
if something is significant it means that
statistically significant = certain within a minimal level of error
type one error
(a) probability of mistakenly rejecting the null hypothesis
type two error
(b) probability of failing to reject when you should have
probability testing
(p) producing a type 1 error
if results are greater than .05 than it is concluded that it is
not significant enough
if results are less than .05 that it is concluded that it is
significant
alpha numerical is what
preset
beta numerical is what
not preset
correlation is what
relationship between two characteristics
if its -1.0 what is it called
perfect negative correlation
if its 1.0 what is it called
perfect positive correlation
little r =
Pearson product correlation
reliability of r is symbolled by what
r
interpreting the meaningfulness of r is symbolled by what
r2
regression equation is also called what
prediction
simple regression is explained as
predicting 1 value from 1 variable y(value)= a +bx(variable)
partial correclation
taking a third variable out of the relationship between two variables
semipartial correlation
removing the influence of a third variable on only one of the two variables in a relationship.
multiple regression
correlating more than one predictor with a continuous criterion variable.
types of predictors
forward selection, backward selection, stepwise, hierarchical, maximum r2
parametric testing samples (has all with one dependent variable)
independent sample t test, paired sample t test
non parametric testing samples
mann whitiney u test, wilcoxisum signed rank test
when is a t test aproperatie
when only two things are being compared to each other
paired(matched) sample t test is what
testing one condition against another against themselves
one-way anoua parimetric
kruskl- wallis anoua
two way anoua pariemtric
freidmans awoua
manoua
non parametric greater than one dependent variable
total variance=
true variance + error variance
test of significance=
true variance / error variance
test of meaningfulness =
true variance / total variance
degree of freedom
extent to which data can vary (n-1)
f-test
mean square between groups/ mean square within groups
post hoc test
follow up to a significant anova result
duncan
most difference
scheffe
least difference
turkey
honest significant difference
significance difference =
less than .05
after significant anouva result what do you do
post talk
why dont we do serial t testing
because there will be too much of error (inflated type one error)
chi square test is what kind of test
non parametric test
why do we square things in stats
to remove the negative
regression equations do what
prediction
stepwise regression
to determine if things are good predictors adding things to the data
logical validity
does it appear to be valid on the surface level, logical subject has to believe it is logical
content validity
does the study have the right content and all the right information included in the test.
criterion validity
dependent variable, the thing of interest. how valid is the thing your measuring
construct validity
was the test constructed correctly
sources of measurement error
tester, calibration, subject, test design, random error
reliable measure is considered
constant and stable
interclass is symbolized by
r compared one test with repeated process within
intraclass is symbolized by
R between compared trial to trial with more in def analysis
true error variance is
subject to subject
