Introduction To Research Data Analysis And Presentation

Front 1

Purpose of analyzing data

Back 1

Obtain usable and useful information

Front 2

The analysis, irrespective of whether the data is qualitative or quantitative may:

Back 2

Describe and summarize the data

Identify relationships within the variables

Compare variables

Identigy the difference between variables

Forecast outcomes

Front 3

Common misconceptons in data analysis

Back 3

Analysis comes at the end after all the data are collected

Quantitative analysis is the most accurate type of data analysis

Data have their own mening

Stating limitations to analysis weakens the evaluation

Computer analysis is always easier and better

Front 4

Organization of data

Back 4

Organize all forms/questionnaire in one place

Check for completeness and accuracy

Front 5

If the normative value is 120 over 80, and the results is closer to 120/80, the results is (Accurate, Precise)

Back 5

Accurate

Front 6

If the results are close to the normative value and the values are close to each other, accuracy and precision are described as

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High accuracy, High precision

Front 7

If the results are not close to the normative value but has significantly close values, the accuracy and precision will be described as

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Low accuracy, high precision

Front 8

Ways to know how to make the accuracy and precision higher

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Check questionnaires for uncomplete/unreliable questions

Methodology - specfically the pre implementation phase

Front 9

Statistical data

Back 9

Demographics/Descriptibe statistics

Frequency

Front 10

This type of statistical data simply means how many individuals checked each answer

Back 10

Frequency

Front 11

Frequency is determined in?

Back 11

Measurement scales

Front 12

Types of measurement scale

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Parametric

Nonparametric

Front 13

This type of measurement scale has true numerical value, ratio, or interval

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Parametric

Front 14

This type of measurement scale has no true numerical value, nominal, or ordinal since it has descriptor

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Nonparametric

Front 15

This measurement scale is used for labeling variables without any quantitative value, no true numerical value, no quantitative value

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Nominal

Front 16

This measurement scale has the data ordered or ranked on a scale, has true numerial value, has order and rank, and has descriptor

Back 16

Ordinal

Front 17

This defines if you have top or least scale

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Descriptor

Front 18

This measurement scale is a numeric scale in which not only the order is known, but also the exact differences between value

Back 18

Interval

Front 19

What scale of measurement is being described: Numbers assigned to runners

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Nominal

Front 20

What scale of measurement is being described: rank order of winners

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Ordinal

Front 21

What scale of measurement is being described: performance rating on a 0 - 10 scales

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Interval

Front 22

What scale of measurement is being described: Time finish in

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Ratio

Front 23

This measurement scale allows data to be described in terms of its reltionship to a ratio, and has an absolute zero

Back 23

Ratio

Front 24

If you want to know the proportion of people who answered in a certain way

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Percentage

Front 25

This is the average core or average score

Back 25

Mean

Front 26

Things to consider when determining the mean

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Highly sentitive to extreme scores, which have the potential to skew the results

Be sure that the average is meaningful

Front 27

This is the middle value in a range of values or scores

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Median

Front 28

This is the frequent score

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Mode

Front 29

This is the range of answered or scores

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Range

Front 30

This is the degree to which a response varies from the mean

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Standard deviation

Front 31

This statistics is applicable if the SOP has no correlation, difference, predictive value, or forecasting, meaning descriptive stats such as frequency, percentage, should be used

Back 31

Descriptive statistics

Front 32

Descriptive statistics comprises of

Back 32

Collecting

Organising

Summarising

Presenting data

Front 33

This statistics is applicable if there is difference or relationship, and is used to challenge or test hypothesis

Back 33

Inferential statistics

Front 34

Inferential statistics comprises of

Back 34

Making inference

Hypothesis testing

Determining relationships

Making predictions

Front 35

This type of correlational test look for an assocation between variables

Back 35

Correlational

Front 36

This type of correlational test tests for the strength of the association between 2 continuous variables, for parametric variables, and have true numerical value (with interval or ratio)

Back 36

Pearson correlation

Front 37

This correlational test tests for the strength of the association between 2 ordinal variables who does rely on the assumption of normal distributed data, for non parametric data, like ordinal and nominal data

Back 37

Spearman correlation

Front 38

This correlational test tests for the strength of the association between 2 categorical variables, for dichotomous data, to compare 2 data with 2 possible answers only

Back 38

Chi-square

Front 39

These tests looks for the difference between the means of variabe

Back 39

Comparison of means

Front 40

ANOVA means

Back 40

Analysis of variance

Front 41

T/F ANOVA is parametric such as interval and ratio

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T

Front 42

This comparison of means tests for difference between two related variable before and after in one subject

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Paired T-test

Front 43

This comparison of means is for two separate subjects, or difference between 2 indepedent variables

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Independent T-test

Front 44

This comparison of means tests the difference between group means after any other variance in the outcome variable is accounted for, used for 3 or more subjects

Back 44

ANOVA

Front 45

For non parametric comparisons of means tests what can we use

Back 45

Wilcoxon signed rank test

Mann Whitney U test

Kruskal Wallis Test

Friedman tst

Front 46

Counterpart for paired t-test

Back 46

Wilcoxon signed rank test

Front 47

counterpart for independent t-test

Back 47

Mann Whitney U Test

Front 48

ANOVA counterpart

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Kruskal Wallis Test

Front 49

Counterpart for Repeated measures ANOVA

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Friedman test

Front 50

These tests assess if change in one variable predicts change in another variable

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Regression tests

Front 51

This regression test tests how change in the predictor variable predicts the level of change in the outcome variable, if the value can be predicted under variables

Back 51

Simple regression

Front 52

How many variables to know if 1 variable can predict 1 outcome variable

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2

Front 53

This regression test tests how change in the combination of two or more predictor variable predict the level of change in the outcome variable, checks if 1 variable/combination of variable can predict 1 outcome variable

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Multiple regression

Front 54

Simple or multiple regression: Your grades can predict if you will pass the boards examinations

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Simple regression analysis

Front 55

Simple or multiple regression: Grades from 1st to 3rd years, if these can predict your grades for internship, or if it enough to pass the board examinations

Back 55

Multiple regressin

Front 56

This is an in-text citations of authors we used in discussing RRL

Back 56

Citation

Front 57

Citation comprises of

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Reference/Bibliography

Front 58

In this type of citation, there is use of direct quotation from an original source, give the author(s) credit for her/his/their words as follows

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Direct quotation

Front 59

We should include this in the direct quotation to enable the readers to find it

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Page numbers

Front 60

This type of citation means translating the thought into your own words from the source, then give the author(s) credit for their ideas

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Paraphrase

Front 61

T/F paraphrase can be put in the beginning only

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F - can be in the beginning or in the end

Front 62

In this type of citation, you refer to a personal communication (e.g email message, or conversation) that is not available to your readers

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Personal Communication

Front 63

In this type of citation, you refer to a primary source you read about in a secondary source

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Prima and secondary source

Front 64

In this type of citation, you refer to an online source

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Online source

Front 65

N.D means?

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No date

Front 66

This is used between authors when their names are within parentheses

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Ampersand (e.g (Smith & Jones, 1999)

Front 67

This is used when the author(s) name are not within parentheses

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Regular And (e.g Smith and Jones (1999))

Front 68

If the sources does not have a date, replace the date in the citation with?

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N.D

Front 69

Purpose of reference

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Provide readers with the information necessary to retrieve the sources you cited in the body of your paper

Front 70

Important questions to answer in referencing

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Who wrote this document

When was it written

What was its title

Where was it published

Who published it

Front 71

In the new APA formatting and citation, this is not included anymore

Back 71

Where was it published

Front 72

Important details in citation

Back 72

Spacing

Use of ampersand

Use of italics

Hanging indent

No author

Online

Front 73

When to use an ampersand

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Before the final author and precede it with a coma

Front 74

This part of citation should be italicized

Back 74

title of the book

Front 75

Hanging indent format

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Format > paragraph > indent and spacing > special > hanging

Front 76

When the book has no author, what should you do

Back 76

Use the publisher as the author and replace the publisher with the word author

Front 77

When referencing an online source, follow the title with the date you

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Retrieved it and its URL

Front 78

T/F You should put a period after the URL

Back 78

F