**ESTABLISHING PATTERNS OR **ESTABLISHING PATTERNS OR TRENDS IN THE DATA COLLECTED**TRENDS IN THE DATA COLLECTED**

BYBYDR. ARTEMIO P. SEATRIZDR. ARTEMIO P. SEATRIZ

MMSU-CTEMMSU-CTELAOAG CITYLAOAG CITY

IntroductionIntroduction

In the conduct of your In the conduct of your experiment or your investigation, you experiment or your investigation, you collected a lot of information. What collected a lot of information. What do you call these pieces of do you call these pieces of information that you have collected information that you have collected from your experiment? These are from your experiment? These are called data. Since these data are called data. Since these data are unorganized and unordered, they are unorganized and unordered, they are called called raw data.raw data.

Generally, it is very hard to Generally, it is very hard to interpret data in its raw form – interpret data in its raw form – unorganized and unordered data. unorganized and unordered data. Data in its raw form have little or no Data in its raw form have little or no meaning at all. So, as the meaning at all. So, as the investigator you should do something investigator you should do something to make the gathered data to make the gathered data meaningful.meaningful.

In this seminar-In this seminar-workshop, we are going to workshop, we are going to look at the different forms look at the different forms of data, how to present of data, how to present data, and how to make data, and how to make your collected data your collected data meaningful.meaningful.

I. Classifying DataI. Classifying DataData may be classified in different Data may be classified in different

ways:ways:A. Quantitative Data vs. QualitativeA. Quantitative Data vs. Qualitative DataData

Quantitative dataQuantitative data – data gathered – data gathered based on measurementbased on measurement or counting like height ofor counting like height of plant, weight of plant,plant, weight of plant, number of seedlings in anumber of seedlings in a plotplot

Qualitative dataQualitative data – data gathered – data gathered

using a non-standardusing a non-standard

scale or unequal intervalsscale or unequal intervals

or discrete categoriesor discrete categories

like leaf conditionlike leaf condition

categorized ascategorized as

healthy or not healthy;healthy or not healthy;

color of leaves as green,color of leaves as green,

dark green, light green ordark green, light green or

yellow green, etc.yellow green, etc.

B. Continuous Data vs.B. Continuous Data vs.

Discontinuous/Discrete DataDiscontinuous/Discrete Data

Continuous dataContinuous data – data gathered – data gathered

through measurement likethrough measurement like

heights of plants, weights ofheights of plants, weights of

plants, flowering time, etc.plants, flowering time, etc.

Discrete dataDiscrete data – gathered obtained – gathered obtained

through counting likethrough counting like

number of leaves per plant, number of leaves per plant,

number of podsnumber of pods

produced per plant,produced per plant,

number of mangoes pernumber of mangoes per

basket, number ofbasket, number of

seedlings in a plot, etc.seedlings in a plot, etc.

Data may also classified according Data may also classified according to scales of measurement – nominal, to scales of measurement – nominal, ordinal, interval or ratio.ordinal, interval or ratio.

Nominal dataNominal data – data where – data where objects are placed in discreteobjects are placed in discrete categories which cannot becategories which cannot be ranked in ascending or ranked in ascending or descending order like brand ofdescending order like brand of detergents, color of leaves, etc.detergents, color of leaves, etc.

Ordinal dataOrdinal data – data where – data where objects are placed intoobjects are placed into categories that can be rankedcategories that can be ranked or ordered in an ascending oror ordered in an ascending or descending manner likedescending manner like condition of leaves of plantscondition of leaves of plants categorized as healthy or notcategorized as healthy or not healthy;healthy;

Interval dataInterval data – data collected using – data collected using a scale with equal interval but noa scale with equal interval but no absolute zero value likeabsolute zero value like temperature in temperature in 00C.C.

Ratio dataRatio data – data collected using a – data collected using a

scale of equal interval and anscale of equal interval and an

absolute zero like height ofabsolute zero like height of

plants, weights of plants,plants, weights of plants,

number of leaves per plant, etc.number of leaves per plant, etc.

II. Tabulating and Graphing the DataII. Tabulating and Graphing the Data

Although you have classified Although you have classified your data as quantitative or qualitative; your data as quantitative or qualitative; discrete or continuous; nominal, ordinal, discrete or continuous; nominal, ordinal, interval or ratio, they do not say interval or ratio, they do not say anything yet or they do not have any anything yet or they do not have any meaning yet.meaning yet.

To be able to extract meaning from To be able to extract meaning from your data, you have to organize or your data, you have to organize or transform your raw data in to a more transform your raw data in to a more compact or organized way.compact or organized way.

Tabular PresentationTabular Presentation – presenting data in – presenting data in rows and columns rows and columns Table 1. Height of plants.Table 1. Height of plants. ================================================ Plant No. Height of Plants (cm)Plant No. Height of Plants (cm) Horse manure UreaHorse manure Urea -------------------------------------------------------------------------------------- 1 26 25.71 26 25.7 2 23 26.22 23 26.2 3 23.5 24.63 23.5 24.6 4 25.3 27.04 25.3 27.0 5 26.5 25.85 26.5 25.8 6 24.8 27.66 24.8 27.6

7 25.6 27.47 25.6 27.4====================================================

Graphical PresentationGraphical Presentation – pictorial or – pictorial or

visual representation of datavisual representation of data

- pictures are easier to- pictures are easier to

understand than wordsunderstand than words

* What are the different kinds of* What are the different kinds of

graphs? graphs?

* What is the appropriate type of* What is the appropriate type of

graph for a certain set of data?graph for a certain set of data?

III. DESCRIBING DATAIII. DESCRIBING DATA Two ways of describing a set of Two ways of describing a set of

quantitative or numerical data:quantitative or numerical data:

1. Measures of Central1. Measures of Central TendencyTendency

MeanMean MedianMedian ModeMode

2. Measures of Variation2. Measures of Variation

RangeRange

Quartile DeviationQuartile Deviation

Mean DeviationMean Deviation

VarianceVariance

Standard DeviationStandard Deviation

IV. INTERPRETING QUALITATIVE IV. INTERPRETING QUALITATIVE AND QUANTITATIVE DATAAND QUANTITATIVE DATA

After you have organized and After you have organized and presented your data in a more presented your data in a more compact form, you are now ready compact form, you are now ready to analyze, interpret and to analyze, interpret and synthesize the relationships synthesize the relationships between and among your data between and among your data variables.variables.

Guide in analyzing andGuide in analyzing and

interpreting datainterpreting data

1. Write a topic sentence stating the 1. Write a topic sentence stating the independent and dependent independent and dependent variables. Give reference to table variables. Give reference to table and graph.and graph.

2. Write a sentence comparing the 2. Write a sentence comparing the measure of central tendency of the measure of central tendency of the collected data.collected data.

3. Write a sentence describing the 3. Write a sentence describing the variation.variation.

4. Write a sentence stating how the 4. Write a sentence stating how the data support the hypothesis.data support the hypothesis.

Example:Example:The responses of plants to The responses of plants to

compost and urea were investigated. compost and urea were investigated. The responses measured in the The responses measured in the study were height of plants, how long study were height of plants, how long plants started to flower, number of plants started to flower, number of pods produced per plant and total pods produced per plant and total weight of plants per plot. The data weight of plants per plot. The data are shown in Table 1.are shown in Table 1.

It can be noted from the table that the It can be noted from the table that the mean height of plants grown in soil with mean height of plants grown in soil with fertilizer was higher than that of plants in fertilizer was higher than that of plants in the control group (without fertilizer). The the control group (without fertilizer). The mean height of plants grown in soil with mean height of plants grown in soil with horse manure was higher than that in horse manure was higher than that in urea. The bar graph shows the trend.urea. The bar graph shows the trend.

The range of plant height in the control The range of plant height in the control group (without fertilizer) was greater that group (without fertilizer) was greater that that of the plants grown in soil with horse that of the plants grown in soil with horse manure and urea.manure and urea.

The data supported that The data supported that hypothesis that plants grown with hypothesis that plants grown with fertilizer would be taller than plants fertilizer would be taller than plants grown without fertilizer. The grown without fertilizer. The flowering time would also be shorter flowering time would also be shorter with the use of fertilizer.with the use of fertilizer.

V. TESTING HYPOTHESISV. TESTING HYPOTHESIS

After determining the After determining the measures of central tendency and measures of central tendency and variation of your data, you can variation of your data, you can present a summary table showing present a summary table showing these measures showing these these measures showing these descriptive information.descriptive information.

Example:Example:Table 2. Mean heights of plants grown in soil withTable 2. Mean heights of plants grown in soil with

and without fertilizers.and without fertilizers.

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Descriptive Without Horse UreaDescriptive Without Horse Urea

Information Fertilizer Manure (cm)Information Fertilizer Manure (cm)

(cm) (cm)(cm) (cm)

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Mean 20.60 32.60 30.80Mean 20.60 32.60 30.80

Range 5 8 6Range 5 8 6

Maximum 21 33 32Maximum 21 33 32

Minimum 16 25 26Minimum 16 25 26

Number of Plants 7 7 7Number of Plants 7 7 7

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Consider the following questions:Consider the following questions:

1. Are there significant differences in 1. Are there significant differences in the mean heights of the three sets the mean heights of the three sets of plants?of plants?

2. Can you conclude that fertilizers 2. Can you conclude that fertilizers improve plant height?improve plant height?

3. Are the differences due to the 3. Are the differences due to the application of fertilizer alone or is it application of fertilizer alone or is it by chance?by chance?

How do you answer these How do you answer these questions? questions?

To be able to answer these To be able to answer these questions, you should use questions, you should use inferential statistics particularly inferential statistics particularly the area of hypothesis testing. the area of hypothesis testing.

List of some appropriateList of some appropriate

statistical toolsstatistical tools============================================================

Category Analysis Quantitative QualitativeCategory Analysis Quantitative Qualitative

of Data Data Dataof Data Data Data

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Descriptive Measure of Mean Median Descriptive Measure of Mean Median

statistics Central Range Modestatistics Central Range Mode

Tendency/ Variance FrequencyTendency/ Variance Frequency

Variation Standard distributionVariation Standard distribution

deviationdeviation

Inferential Statistical Parametric Non-Inferential Statistical Parametric Non- Statistics Test parametricStatistics Test parametric

Two dependentTwo dependent samples t-test Wilcoxonsamples t-test Wilcoxon testtestTwo independent t-testTwo independent t-test z-testz-test F-testF-testThree or more inde-Three or more inde- pendent samples ANOVApendent samples ANOVA F-testF-test==============================================================

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