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Introduction to Statistics in Kinesiology, Kin 206 > Module 1, Intro to Statistics & the Scientific Method > Flashcards

Module 1, Intro to Statistics & the Scientific Method Flashcards

1
Q

Quantitative

A

the data is in numbers

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2
Q

What is Empirical Research?

A

empirical research: any activity in which data are collected from some area of experience and then conclusions are drawn from the data about the area of experience
- captivates both quantitative and qualitative research
- it means we collect data and make sense of it (interpret)
- through stats we make sense of that data - a tool we use to help make sense of quantitative research

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3
Q

Branch of Mathematics

A
  1. collection - quantitative data
  2. analysis (for example: going through procedures of doing a particular mathematical operation on that data to come up with an average)
  3. *interpretation - what do the numbers mean (something is just a number until it is interpreted)
  4. presentation of numerical data
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4
Q

The Scientific Method (5 steps)

A
  1. developing a research hypothesis to be tested (distinguishing between variables & types of hypotheses)
  2. collecting data (sample vs. population, levels of measurement & experimental vs non-experimental methods)
  3. analyzing data (descriptive vs inferential statistics)
  4. conclusions about research hypotheses (use of language)
  5. communicating findings
    * loop back to research hypothesis to draw conclusions (loop 4 to 1)
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5
Q

What is a research hypothesis?

A

research hypothesis: a statement regarding an expected or predicted relationship between variables (they are very specific as they have to be testable)

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6
Q

Variable

A

variable: a property or characteristic that can take on different values

  • variables are related to quantitative research as they take on different values (data will be in numbers)
  • a variable needs to vary - cannot just take on one value (ex. class attendance (whether you attended or not) varies, not who attended today)
  • measure is a key word: for instance, measure the degree to which someone is experiencing depressive symptoms (special case where you cannot directly measure something)
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7
Q

Construct

A

constructs is when you cannot directly measure a variable, they are theoretical (that means the definition of what it is can change)
‣ mental toughness - there
are many definitions of it
making it a construct
‣ constructs are a special
type of variable

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8
Q

How do we determine what the research hypothesis is?

A

research hypotheses can come from a variety of sources:
1. identifying a question or issue to be examined
2. *review and evaluating relevant theories and research (help guide us in selecting what variables we should use and how they connect)

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9
Q

How do we Distinguish Between Variables? (IV/DV)

A

independent variable (IV): the variable manipulated by the researcher
dependent variable (DV): the variable measured by the researcher
- want to know the effect of IV on the DV
example: the thing that is being manipulated is the number of grams of proteins (IV) (after a workout) - 20g, 40g, 60g (this variable is varying) - levels of an independent variable (same) are the different numbers
the number of groups you have is the same as the number of levels of the IV - 3 levels in this case

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10
Q

Directional Hypothesis

A

is going to state whether a group scores higher or lower on an outcome variable compared to another group and could indicate whether there is a positive or negative relationship
- mostly there are directional hypotheses that are used to see which group will do better

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11
Q

Non-Directional Hypothesis

A

would state that there is a relationship between the variables but do not know if there is a positive or negative relationship OR we do not know which group will score higher or lower on the outcome variable (just know there will be a difference)

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12
Q

Among both directional and non-directional hypothesis which is often utilized more?

A

directional

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13
Q

Research Question: Do youth soccer players who do not wear head protection experience a different rate of concussions than those who wear head protection?
(IV/DV/HOW MANY LEVELS/WHAT LEVEL OF MEASUREMENT)

A

DV = # of concussion (ratio level of measurement - true zero point)

IV = presence of head protection (variables have to vary) (2 levels of the IV - nominal/categorical)
- the presence of head protection will have an impact on the number of concussions (hypothesis causal relation) IV -> DV

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14
Q

Research Hypotheses: There will be a difference in the number of concussions between youth soccer players who wear head protection and those who do not
research
Q: IS IT DIRECTIONAL OR NON-DIRECTIONAL HYPOTHESIS?

A

non-directional (difference)

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15
Q

Research Hypotheses: Youth soccer players who wear head protection will have fewer concussions than those who do not wear head protection
Q: IS IT DIRECTIONAL OR NON-DIRECTIONAL HYPOTHESIS?

A

directional (fewer)
- are stating who is going to score higher or lower on the outcome variable

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16
Q

Research Hypotheses

A
  • the relationship between variables written with words
    ◦ A related to B
    ◦ A causes B
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17
Q

Statistical Hypotheses

A
  • are what we are actually going to test, are mathematical representations of what we are testing
  • will not be in words rather mathematical symbols
18
Q

Statistical - Null Hypothesis

A
  • states there is NO relationship
  • statistical hypothesis to be rejected
  • says nothing will happen/no relationship
19
Q

Statistical - Alternative Hypothesis

A
  • often associated with research hypothesis, in that it states there will be a relationship or a difference
  • mathematical symbols
  • how you state them will change/differ depending on the test you will conduct or do (for both)
20
Q

Research Hypotheses: There will be no difference in the number of concussions between youth soccer players who wear head protection and those who do not
Q: IS THIS NULL OR ALTERNATIVE HYPOTHESES?

A

null hypothesis (states there will b NO dfference)

21
Q

Population

A

population: the total number of possible units or elements that could be included in a study
◦ theoretical population
◦ often times the unit is people
(other examples include team,
sport, animals, league,
university)

22
Q

Sample

A

sample: a subset of the population used to represent the population
- if you are in the sample you are also in the population
- sample characteristics = population characteristics (sample characteristic is representative of population characteristics)

23
Q

Measurement

A

concerned with the methods to provide descriptions of the degree (value) to which an individual (or place or thing) possesses a defined characteristic (property)
- the quality of data and measurement is very important so the interpretation is accurate

24
Q

Why does the level of measurement matter? the type of scale effects:

A
  • what we can do statistically with the data
  • the mathematical operations that can be performed (the certain level of measurement has certain assumptions attached to it so we cannot do certain math operations on that scale sometimes)
  • how we interpret data
  • whether differences (between individuals or groups) are meaningful
  • whether larger/smaller numbers are “better”?
25
Q

What are the 4 Levels of Measurement used for variables?

A
  • measurement is the cornerstone to quantitative research
    levels of measurement:
    1. nominal
    2. ordinal
    3. interval
    4. ratio
26
Q

Nominal (categorical)

A

values differ in category or type (a numerical value is used to denote a category)

  • the number itself is not valuable where you can assign numbers to sports to categorize them for instance as the not indicate rank or order
  • the scale only represents a category and does not represent rank
  • independent variables are typically measured at a nominal level of measurement - typically based on categories - a control group and experimental are also considered a category
27
Q

Levels of Measurement (nominal)

A

refers to the number of levels of measurement within a nominal variable
- amounts to the number of categories
◦ control groups are included in
levels of measurement
◦ level of measurement is not an
actual variable (for example,
football is a level of
measurement but variable is
the sport type)

28
Q

Ordinal

A

values can be placed in order relative to other values

  • think about ranking (we only know rank not how good or bad someone was in comparison to someone else)
29
Q

Interval

A

values are equally placed along a numeric continuum - no absolute zero

  • temperature for example, there is zero degrees but it does not denote that there is no temperature and thus no absolute zero
    rating scales (1-5) - numeric continuum -> this type of scale can be seen as ordinal or interval
  • reverse coded - the value becomes the opposite where 5 becomes 1 and 1 becomes 5 (pay attention to how things are worded)
30
Q

Ratio

A

values are equally spaced on a numeric continuum - true zero point

  • what is measured is the distance you can run in 12 minutes (the distance (m) is the scale) - the scale itself has a true zero - starting at zero
31
Q

Likert Type Scales

A

the measurement scale is arbitrary and then you have different response options
- anchors on other ends like strongly agree and strongly disagree (usually disagreement scales)
- if you have likert type scale that has 4 or less response options = ordinal level of measurement
- if you have a likert type scale that has 5 or more response options = interval level of measurement
◦ this rule of thumb is only for
likert type scales

32
Q

Discrete Variables

A
  • no underlying continuum exists
  • measure classifies items into non-overlapping categories
    ◦ nominal/categorical and
    ordinal scales
33
Q

Continuous Variables)

A
  • underlaying continuum
  • eg. aggression, reaction time, mental toughness
  • interval and ratio level of measurement
    there are different stats that we would use for continuous or discrete particularly when looking at outcome variable or IV
34
Q

Descriptive Statistics

A
  • organize, summarize and describe the data that has been collected
    ◦ collecting data from the
    sample usually
    ◦ measures of central
    tendencies - the average or
    mean, median and mode
    (most frequently occurring
    data)
    ◦ measures of variability -
    standard deviation, range and
    the variance
35
Q

Inferential Statistics

A
  • test hypotheses and draw conclusions about the data collected from the sample
  • inferences from samples to populations (interested in making inferences about a population)
    ◦ underpinning assumption mis
    that you have a representative
    sample of whats actual going
    on in the population
  • mean arm hang of this sample could be used to represent the mean arm hang of all female Canadians aged 20-29
36
Q

Conclusions about Research Hypotheses - Use of Language

A
  • ask whether or not the results support the research hypothesis
    ◦ there is an important
    distinction between support
    and prove
    ◦ prove is based on certainty but
    we want to look at probability
    ◦ inferential statistics is based
    on probability
37
Q

Communicating Findings

A
  • communicating the results and interpretation of the results is important within the field
    ◦ publishing academic journals,
    graph, infographic (visuals),
    present at conferences, social
    media
38
Q

Experimental Research Methods

A

are methods designed to test causal relationships between variables—more specifically, whether changes in independent variables produce or cause changes in dependent variables. To make inferences about cause-effect relationships, researchers conducting an experiment must first eliminate all other possible causes or explanations for changes in the dependent

39
Q

Confounding Variable

A

is a variable related to an independent variable that provides an alternative explanation for the relationship between the independent and dependent variables

40
Q

Non Experimental Research Methods

A

non-experimental research methods are research methods designed to measure naturally occurring relationships between variables without having the ability to infer cause-effect relationships

some of the most common types of nonexperimental research designs include quasi-experiments, survey research, observational research, and archival research

41
Q

Quasi-experimental research

A

compares naturally formed or preexisting groups rather than employing random assignment to conditions

Introduction to Statistics in Kinesiology, Kin 206 (9 decks)

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