Download Chapter 1: Why is my evil professor forcing me to learn statistics?

Independent & Dependent Variables
• Independent variable: A variable thought to be the
cause of some effect. Used in experimental
research to denote a variable that the
experimenter manipulated.
• Dependent variable: A variable thought to be
affected by changes in the independent variable.
This is the outcome variable.
• Predictor variable: A variable thought to predict an
outcome variable. Basically an independent
variable.
• Outcome variable: A variable thought to change as
a function of change in a predictor variable.
Identify Independent & Dependent Variables
• The purpose of this experiment was to determine
the effects of exercise on body fat.
• The purpose of this experiment was to determine
the effects of power training on vertical jump.
• The purpose of this experiment was to determine
the relationship between diet and body
composition.
From your initial observation you generate
explanations, or theories, of those observations, from
which you can make predictions (hypotheses).
Scientific statements are ones that can be verified
(tested) using empirical evidence.
Levels of Measurement
• Categorical
– Binary variable: Only 2 categories (male, female)
– Nominal variable: More than two categories: young,
middle age, elderly.
– Ordinal variable: The variable is ordered by some
attribute, such as pain. (each interval does not
represent and EQUAL distance). Ex: RPE & Pain ratings
• Continuous
– Interval variable: Equal intervals on the variable
represent an equal difference. Temperature is an
interval variable.
– Ratio variable: Equal intervals represent equal
difference. Zero is an absence of the variable, ratio
values are meaningful.
Measurement Error
• Measurement error is the discrepancy between a variables
actual value and its measured value.
• Some variables more prone to errors than others: attitude,
pain, volume of gas expired, blood pressure, height and
weight.
• Factors that can influence measurement error:
– Accuracy of instruments
– Random variation in the variable
– Adherence to sound measurement principles
Validity and Reliability
• Validity refers to whether an instrument
actually measures what it is designed to
measure.
– DEXA, hydrostatic, and skinfolds can all
measure the percent body fat. DEXA has the
highest validity of the three.
• Reliability refers to the consistency of the
instrument.
• The easiest way to test reliability is to
measure the same people twice (testretest reliability).
Correlational vs Experimental Research
• In correlational research we observe what
goes on without directly interfering with it.
– Ex: what is the relationship between anxiety
and performance?
• In experimental research we manipulate
one variable to see its effects on another.
– Ex: What is the effect of caffeine on reaction
time?
Experimental Research
• Confounding variable: a variable that we may or
may not have measured other than the predictor
variables, that potentially affects the outcome.
• Counterbalanced: the process of systematically
varying the order of conditions. ½ begin with cond
1, ½ begin with cond 2.
• Randomization: random assignment of subjects to
different groups. [Be sure you understand the
difference between counterbalanced and randomization]
• The goal in experimental research is to rule out all
confounding variables so that any changes in the
dependent variable can be attributed to the
treatment.
Experimental Research (con’t)
• Two methods of data collection
1. Using different groups of subjects in each
condition. Referred to as: a between-groups,
between-subjects, or independent groups
design.
2. Using the same subjects in each condition.
Referred to as: a within-subjects or repeated
measures design.
Sources of Variation
• Systematic variation: variation due to
experimental manipulation.
– Effects of caffeine on heart rate. The drug
caffeine should cause variation if all
confounding variables are controlled.
• Unsystematic variation (Random
Variation): variation that exists between
experimental conditions (such as
differences in ability, time of day, etc).
– Some subjects will be affected by caffeine
more than others.
Statistics Quantifies Systematic & Unsystematic Variation
• The role of statistics is to discover how much variation
there is in performance, and then partition the variance to
systematic and unsystematic (random).
• In repeated measures designs, differences between two
conditions can be caused by:
– Manipulation (treatment) applied
– Or any other factor that affects performance over time.
• In an independent design, differences between two
conditions can be caused by:
– Manipulation (treatment) applied
– Differences in the subjects that are Randomly Assigned to the
treatment groups.
• The error variation is always greater in an independent
design.
Randomization
• Randomization is important because it eliminates
most other sources of systematic variation.
• In a repeated-measures design the two most
important sources of systematic variation are:
– Practice effects: subjects get better at the test after
performing the test on the pre-test.
– Boredom effects: subjects perform differently on the
post-test because they are bored or fatigued.
• Counterbalancing helps to minimize the order
effects.
Randomization
• In an independent design subjects must be randomly
assigned to treatment groups to minimize the effect of
confounding variables on systematic variation.
• The purpose of this experiment is to determine the
effects of a 6-week power training program on jumping
power. What will happen if:
1.
2.
3.
10 Olympic volleyball players were assigned to the power
training group, 10 untrained subjects were assigned to the
control group.
10 untrained subjects were assigned to the power training
group, 10 Olympic volleyball players were assigned to the
control group.
20 subjects were randomly assigned to either the power
training group or the control group.
The normal distribution is symmetrical. The
mean, median and mode are all the same value.
The median is the middle score
22, 40, 53, 57, 93, 98, 103, 108, 116, 121, 252
Arrange the scores in ascending order.
Count the number of scores, add 1 then divide by 2.
(n + 1)/2 = (11 + 1)/2 = 6. 98 is the 6th score, so the
median is 98.
For an EVEN number of scores take the ave of the middle two
scores
22, 40, 53, 57, 93, 98, 103, 108, 116, 121
Arrange the scores in ascending order.
Count the number of scores, add 1 then divide by 2.
(n + 1)/2 = (10 + 1)/2 = 5.5. (93 + 98)/2 = 95.5
the median is 95.5.
The Mean
Compute the mean of
22, 40, 53, 57, 93, 98, 103, 108, 116, 121, 252
The Interquartile Range is the middle 50% of the distribution.
Hypothesis Testing
• Null Hypothesis: States the research question indicating
that there is no difference.
• Alternative Hypothesis or Experimental Hypothesis:
States the research question in the direction of the
expected change.
• We cannot prove the experimental hypothesis, but we
can reject the null at some level of probability.
• We cannot talk about the null or the experimental
hypothesis as being true, we can only talk in terms of the
probability of obtaining the results if the null is true.
• In reality we have not proved anything, only the
likelihood that an outcome may occur.