descriptive and inferential statistics part 1 2013 2014
TRANSCRIPT
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
1/134
Descriptive statistics andinferential statistics
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
2/134
Preparing Data for AnalysisScoring proceduresTabulation and coding
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
3/134
What does it means scoring data?Scoring data means that the researcherassigns a numeric score (or value) toeach response category for eachquestion on the test/instrument tocollect the data
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
4/134
Categorizing dataThe statistical tests- depend on the typeof data being collectedIt is important to understand the typesof data before scoring procedure isconducted
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
5/134
Types of categorical and quantifiable data
Data
Categorical Quantifiable
Nominal Ordinal Interval Ratio
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
6/134
What is categorical data?Data which cannot be quantifiednumerically
BUTPlace into sets or categories ( nominaldata ) or ranked in some way ( ordinaldata )
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
7/134
What is quantifiable dataData can be measured numericallyMore preciseConsist of interval data and ratio data
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
8/134
Four kinds of measurement
scalesNominalOrdinalIntervalRatio
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
9/134
Nominal data A name value or category with no orderor ranking
Example:-Type of schoolTypes of teaching methodGenderRace
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
10/134
Ordinal dataComprises an ordering or ranking ofvalues
ALTHOUGHThe ranks are not intended to be equal(for example, an attitude questionnaire)
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
11/134
ExampleHow of often you felt like insulting astudent (Please tick one)Every dayOnce a weekSometimesNever
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
12/134
Other examples of ordinal
dataQuestions that rate the quality ofstudents performance (for example,very good, good, fair, poor)
Agreements of attitude towards science(Strongly agree, Agree, Disagree,Strongly disagree)
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
13/134
Interval dataNumerical values are assigned along aninterval scale withEqual intervalsThere is no zero point where the traitbeing measured does not exist
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
14/134
Number of students scoring withinvarious ranges in IQ test
Scores Frequency76-80 181-85 0
86-90 491-95 1096-100 21
101-105 25106-110 48111-115 18
116-120 11
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
15/134
Other examples of interval dataTemperature
Blood pressure
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
16/134
Ratio dataSame characteristics with interval data
BUTThere is an absolute zero that representsome meaning
Example:-Costs, sales, number of students, number
of teachers,
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
17/134
Types of categorical and quantifiable data
Data
Categorical Quantifiable
Nominal Ordinal Interval Ratio
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
18/134
Example of the scoring dataStudents should be given an opportunityto select a school of their choiceStrongly agree _____
Agree _____Disagree _____Strongly Disagree _____
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
19/134
A numeric score (or value) to
each response category
Strongly agree 4 Agree 3Disagree 2
Strongly Disagree 1
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
20/134
Other example of scoring dataHow of often you felt like insulting astudent (Please tick one)Every dayOnce a weekSometimesNever
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
21/134
A numeric score (or value) to
each response category
Every day 4Once a week 3Sometimes 2
Never 1
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
22/134
An example of multiple choice
questionThe quantity of charge which passesthrough a circuit is measure in
A. AmpsB. VoltsC. Coulombs *D. Watts
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
23/134
A numeric score (or value) to
each response categoryCorrect response- 1 mark,Incorrect response- 0 mark
A. Amps 0B. Volts 0C.
Coulombs 1D. Watts 0
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
24/134
Scoring Procedures for Open
Ended itemsEach participant tests should be scoredin the same way and with one criterionGreatly facilitated if a standardizedinstrument is usedScoring key should be providedRecheck the consistencyClean the data
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
25/134
Clean the dataWhen a large number of variables andmany individual records, it is easy toenter a wrong figure or to miss an entryDo frequency analysis on a column datato throw up any inconsistent/ spurious
figures
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
26/134
Scoring Procedures forMore complex if is involved open endedquestions
Develop a marking scheme Advisable to have at least one other personindependently score some of the tests
Tried out by administering the tests to similarpopulation as one from the actual study
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
27/134
Example of open ended questionDefine population and sample
________________________________ ________________________________ ________________________________
(2 marks)
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
28/134
The marking schemePrecise and complete definition = 2Precise but incomplete definition= 1Incorrect definition= 0
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
29/134
Tabulation and coding After test/instruments have been scoredTransfers to summary data sheet/computer. For example SPSS data sheetOrganize data in the SPPS to facilitatesexamination and analysis of the data
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
30/134
Tabulation and CodingTabulation is organizing data
Identifying all information relevant to the analysis
Separating groups and individuals within groupsListing data in columns
Coding Assigning names to variables
EX1 for pretest scoresSEX for genderEX2 for posttest scores
Objectives 2.1, 2.2, & 2.3
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
31/134
Tabulation and CodingCoding
Assigning identification numbers to
subjects Assigning codes to the values of non-numerical or categorical variables
Gender: 1=Female and 2=Male
Subjects: 1=English, 2=Math, 3=Science, etc.Names: 001=Ahmad, 002=Rahman,003=Salleh, 256=Karim
Objectives 2.2 & 2.3
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
32/134
Example A study investigating the interactionbetween two types of instruction andtwo levels of ability (A 2 x 2 factorialdesign)Four subgroups are involved
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
33/134
68 marks70 marks79 marks
78 marks90 marks60 marks
50 marks40 marks45 marks
60 marks65 marks55 marks
Method A Method B
High ability
Low ability
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
34/134
4 column involvedStudents id Types of instructionLevel of abilityTotal scores
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
35/134
Students id1 represents Ahmad2 represents Bakar3 represents Malik4 represents Abu
Etc..
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
36/134
Types of instructionTwo types of instruction, namely :cooperative and traditional method1 represents cooperative method2 represents traditional method
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
37/134
Level of abilityHigh and low ability1 represents high ability2 represents low ability
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
38/134
Total ScoresExample: 50 items/questionsCorrect answer- 1 mark
Incorrect answer 0 markFull mark: 50 marksExample:-If 20 items are answered correctlyby Ahmad, that means he will get20 marks for his total scores
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
39/134
Another example A study investigating the effect ofschool location on learning motivation
among male and female students
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
40/134
Four columns involvedStudents idSchool locationStudents gender Learning motivation
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
41/134
Students id1 represents Ahmad2 represents Bakar3 represents Malik4 represents AbuEtc..
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
42/134
School locationUrban or rural1 represents urban2 represents rural
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
43/134
Students gender Male and female students1 represent male2 represent female
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
44/134
Learning motivation5 itemsLikert scale
Example:-I like to study in order to get good marks inthe examinationStrongly agree 4
Agree 3Disagree 2Strongly Disagree 1
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
45/134
How to calculate item which have
Likert scale respons
Total up all the items response for eachperson to get the total scoresDivide the total scores by the number ofthe items to get the mean of learningmotivation for each students
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
46/134
Item 1 = 4Item 2 = 3Item 3 = 4Item 4 = 2Item 5 = 1
Total scores= 4+3+4+2+1=14
How many items? 5 itemsMeans scores of learning motivation= 14/5 = 2.5
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
47/134
After you have prepared for dataanalysis, how do you analyse thedata?
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
48/134
How to analyse the dataDescriptive statisticsInferential statistics
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
49/134
Descriptive statisticsDescribe trends in the data to a singlevariable on your instrument
Example:What is the learning motivation ofsecondary school students?
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
50/134
Descriptive statisticsWhat is the learning motivation ofsecondary school students?
In order to answer that, we needdescriptive statistics that indicategeneral tendencies in data, the spread
of scores, or relative position
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
51/134
Central TendencyPurpose to represent the typical scoreattained by subjects
Three common measuresModeMedian
Mean
Objective 4.1
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
52/134
Spread of scores (variability)Purpose to measure the extent towhich scores are spread apart
Four measuresRangeQuartile deviation
VarianceStandard deviation
Objective 5.1
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
53/134
The normal curve
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
54/134
The Normal CurveIf a sufficient number of subjects aremeasure, possibly a variable or
variables yield a normal, bell-shapedcurveIf a variable is normally distributed,
then several things are true
50% of the scores are above the
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
55/134
50% of the scores are above themean and 50% of the scores are
below the mean
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
56/134
The mean, median and the mode
are the same
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
57/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
58/134
The Normal Curve
MostScores Fewer Number of
Subjects who Attained the Scores
Fewer Number ofSubjects who
Attained the Scores
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
59/134
The Normal Curve
MostScores Fewer Number of
Subjects who Attained the Scores
Fewer Number ofSubjects who
Attained the Scores
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
60/134
The Normal Curve
Fewer Number ofSubjects who
Attained the Scores
Fewer Number ofSubjects who
Attained the Scores
MostScores
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
61/134
The Normal Curve
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
62/134
The Normal CurveFourth, the same number, orpercentage, of scores is between the
mean and plus one standard deviation(mean + 1 SD) as is between the meanand minus one standard deviation
(mean 1 SD), and similarly for mean+ SD and mean + SD
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
63/134
If scores are normally distributed
Mean + 1.0 SD = approximately 68% ofthe scores
Mean + 2.0 SD = approximately 95% ofthe scoresMean + 3.0 SD = approximately 99.7%
of the scores
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
64/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
65/134
Skewed DistributionsResearch data usually more or lessapproximate a normal curve
When a distribution is not normal, it issaid to be skewed, and the values ofthe mean, the median and the mode
are differentIn a skewed distribution, there aremore extreme scores at one end than
the other
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
66/134
Skewed DistributionsIf the extreme scores are at lower endof the distribution, the distribution is
said to be negatively skewedIf the extreme scores are at the upper,or higher, end of the distribution, the
distribution is said to be positivelyskewedThe mean is pulled in the direction of
the extreme scores
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
67/134
Which one is positively skewedand negatively skewed?
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
68/134
Skewed DistributionsFor a negatively skewed distribution,the mean is always lower, or smaller
than the medianFor a positively skewed distribution, themean is always higher or greater than
the median
For a negatively skewed
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
69/134
For a negatively skeweddistribution, the mean is always
lower, or smaller than themedian
For a positively skewed distribution
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
70/134
For a positively skewed distribution,the mean is always higher or
greater than the median
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
71/134
Assessing normality using SPSS
Click on AnalyzeClick on Descriptive Statistics , thenExploreClick the variable/s you are interestedClick the arrow button to move theminto Dependent ListClick on the Plots button
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
72/134
Under Descriptive , click theHistogram
Click on Normality Plots with TestClick on ContinueClick OK
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
73/134
Interpretation of output fromexplore
Skewness and kurtosis valuesTest of Normality (Kolmogorov Smirnovstatistic)HistogramNormal Probability plots (Normal Q-QPlots)
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
74/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
75/134
Kurtosis A measure of the peakedness or the flatness of a distribution A kurtosis value near zero (0) indicates ashape close to normal
A positive value of kurtosis indicates a shapeflatter than normal
A positive value of kurtosis indicates a shape
more peaked than normal A range of kurtosis value between -1.0 and+1.0 is considered as excellent, but a valuebetween -2.0 and +2.0 is consideredacceptable
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
76/134
Kurtosis
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
77/134
SkewnessMeasures to what extent a distributionvalues deviates from symmetry around
the mean A value of zero represents a symmetricor evenly balanced distribution
A positive skewness indicates a greaternumber of smaller values
A negative skewness indicates a greaternumber of larger values
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
78/134
Skewness
es o orma y o mogorovS i i i )
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
79/134
y gSmirnov statistic)
Test of Normality which is KolmogorovSmirnov statistic assesses the normality
of the distribution scores A non-significant result (significantvalue of more than 0.05) indicates
normality A significant result (significant value of0.05 or less than 0.05) suggestsviolation of the assumption of normality
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
80/134
Histogram and Normal Q-Q Plots
The actual shape of distribution can be seenin histogram
In order to support the claim that the data isnormally distributed, refer to normal Q-Q plotNormal Q-Q plot- the observed value for eachscore is plotted against the expected valuefrom the normal distribution
A reasonably straight line suggests a normaldistribution
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
81/134
Graphic representationBar chartHistogramPie chart
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
82/134
Inferential statistics
h h f f l
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
83/134
What is the purpose of inferentialstatistics?
To compare two or more groups on theindependent variable in terms of the
dependent variable ( for example: Isthere a significant differencebetween boys and girls on selfesteem ?)
Independent variable : gender (boysand girlsDependent variable : self esteem
I f i l i i i l
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
84/134
Inferential statistics involveshypothesis testing
Null hypothesis: There is no significancedifference between boys and girls on
self esteem Alternative hypothesis: There is asignificant difference between boys and
girls on self esteem
O h f i f i l
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
85/134
Other purpose of inferentialstatistics
Relate two or more variables (forexample: Does self esteem relate to
academic achievement?)Null hypothesis: There is no significantrelationship between self esteem andacademic achievement
Alternative hypothesis: There is asignificant relationship between selfesteem and academic achievement
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
86/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
87/134
Types of Inferential Statistics
Two issues discussedSteps involved in testing for significanceTypes of tests
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
88/134
Steps in Statistical TestingState the null and alternativehypotheses
Set alpha levelIdentify and compute the test statisticCompare the computed test statistic tothe criteria for significance
Objectives 20.1 20.9
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
89/134
Alpha Level
An established probability level whichserves as the criterion to determinewhether to accept or reject the nullhypothesisCommon levels in education
.01
.05 (the most common)
.10
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
90/134
Reject the null hypothesis
If the probability values is less than
or equal to the significance level,then reject the null hypothesis, andconclude that the research findingis statistically significant
Objective 20.9
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
91/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
92/134
Inferential Statistics
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
93/134
T-TestDetermine whether two means aresignificantly different at a selected
probability level
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
94/134
Independent Samples T-TestDetermine whether there is a probablya significant difference between means
of two independent samples
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
95/134
Independent samplesTwo samples that are randomly formedwithout any type of matching
The members of one sample are notrelated to members of the other samplein any systematic way other than they
are selected from the same population
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
96/134
Example
Group 1 Test Scores Group 2 Test Scores
34567
23334
Are these two sets of scores significantlydifferent? They are different, but are they
significantly different?
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
97/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
98/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
99/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
100/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
101/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
102/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
103/134
Presenting the results for
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
104/134
Presenting the results forindependent samples t-test
An independent samples t-test wasconducted to compare the achievement
test scores for boys and girls. Therewas no significant difference in scoresfor boys (M=34.02, SD= 4.91), andgirls (M= 33.17; SD = 5.71; t (434) =1.62, p =0.11).
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
105/134
Non independent sample t-testor
Paired samples t-test
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
106/134
Nonindependent sample t-testWhen samples are not independent, themembers of one group are
systematically related the members of asecond groupThe most familiar example is if the
same group takes the test at twodifferent timesIn SPSS, it is known as Paired Samples
T-Test
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
107/134
Presenting the results for paired
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
108/134
Presenting the results for pairedsamples t-test
A paired samples t-test was conductedto evaluate the impact of the
intervention on students achievementscores. There was statisticallysignificant decrease in achievementscores from Time 1 (M=40.17, SD=5.16) to Time 2 (M= 37.5, SD= 5.15),t(29) = 5.39, p ,0.005.
One Way Analysis of Variance
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
109/134
One Way Analysis of Variance(One Way ANOVA)
To determine whether there is asignificant difference between more
than two means a selected probabilitylevel
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
110/134
ExampleGroup 1 Test
ScoresGroup 2 Test
ScoresGroup 3 Test
Scores
12223
23456
44457
Are these three sets of scores significantlydifferent? They are different, but are they
significantly different?
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
111/134
Multiple comparisonIf the F ratio is determined to benonsignificant, the party is over
But what if it is significant?Multiple comparison are used todetermine which means are significantly
different from other means
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
112/134
ExampleGroup 1 Test
ScoresGroup 2 Test
ScoresGroup 3 Test
Scores1
222
3
2
345
6
4
445
7
ANOVA results show that there are significantdifference between the means of three groups
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
113/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
114/134
The use of Multiple
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
115/134
pComparison
Multiple comparison procedure used todetermine whether the means of:-
- group 1 differs from group 2, OR- group 1 differ from group 3, OR- group 2 differs from group 3?
Example of multiple comparison
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
116/134
Example of multiple comparisontechnique
Tukey TestScheffe Test
Duncan TestBonferroni TestHSD Test
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
117/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
118/134
Presenting the results from one
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
119/134
Presenting the results from oneway ANOVA with post hoc test
A one way between group analysis ofvariance was conducted to explore thedifference of achievement scoresbetween three group (Group 1, Group2, Group 3). There was a statisticallysignificant difference at the p
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
120/134
Presenting the results from oneway ANOVA with post hoc test
Post-hoc comparisons using the Tukeytest indicated that the mean score for
Group 1 (M=21.36, SD= 4.55) wassignificantly different from Group 3 (M=22.96; SD= 4.49). Group 2 (M= 22.10,SD= 4.15) did not differ significantlyfrom either Group 1 or 3.
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
121/134
Two Way ANOVA Analysis of data which involve factorialdesign
What is factorial design?
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
122/134
Factorial designWhen two or more independentvariables involved in a study
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
123/134
ExampleMethod A Method B
High ability
Low ability
2 X 2 Factorial Design
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
124/134
2 ways ANOVADetermine main effect on achievementfor method (determine there is a
significant difference between meanscores of Method A and Method B)
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
125/134
2 ways ANOVADetermine main effect on achievementfor ability (determine there is a
significant difference between meanscores of high and low ability)
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
126/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
127/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
128/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
129/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
130/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
131/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
132/134
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
133/134
Multiple RegressionMore advance than correlation and linearregressionCorrelation- relationship between two
variable (Ex: relationship between attitudetowards learning and academic achievement)Linear regression- the relationship betweenpredictor variable and dependent variable(Ex: Can attitude towards learning predictacademic achievement of students?)
-
8/12/2019 Descriptive and Inferential Statistics Part 1 2013 2014
134/134
Multiple RegressionMultiple regression- a combination of twoor more variables to predict a dependentvariable
(Ex: Can attitude towards learning andthinking ability predict academicachievement of students?)