DATA ANALYSIS and INTERPRETATION DR. KHAIRUL FARIHAN KASIM SCHOOL OF BIOPROCESS ENGINEERING UNIVERSITI MALAYSIA PERLIS

Recaps: Methodology

Clear subheadings Describe methods in the past tense Methods must be described in sufficient detail so that other researchers can reproduce the experiment Describe statistical tests used Include setup figures if necessary Methods How did you carry out your work?

3.1 Microbial strains and media Escherichia coli NovaBlue (Novagen, Inc., Madison, WI) was used as the host strain for recombinant DNA manipulation. E. coli was grown in Luria-Bertani medium (10 g/L peptone, 5 g/L yeast extract, and 5 g/L sodium chloride) containing 100 mg/L ampicillin. S. cerevisiae strains were routinely cultivated at 30ºC in synthetic medium [SD medium; 6.7 g/L yeast nitrogen base without amino acids (Difco Laboratories, Detroit, MI), 20 g/L glucose] supplemented with appropriate amino acids and nucleotides, and in YPD medium (20 g/L peptone, 10 g/L yeast extract, 20 g/L glucose). Materials and Method Example

CHAPTER 4: RESULTS AND DISCUSSION Presents the findings of the study. Presentation should be clear and scholarly done and may come in the form of tables, figures or charts. Tables and graphs are both ways to organize and arrange data so that it is more easily understood by the viewer. Tables and graphs are related in the sense that the information used in tables is frequently also used for the basis of graphs.

Analysis refers to the skill of the researcher in describing, delineating similarities and differences, highlighting the significant findings or data and ability to extract information or message out of the presented data. Interpretation is the explanations or suggestions inferred from the data, their implications but not conclusions.

PRESENTATION OF FINDINGS – HOW?? VERBAL Describes Explain SYMBOLIC Graphic Tables/Graphs Statistical values

Preparing the Tables and Figure

The rules keep format clear and simple. line up decimal places, note units clearly, use a large enough typeface construct a clean orderly arrangement of rows and columns. Do not construct a table/figure unless repetitive data must be presented

How to design effective tables

Two of the columns give standard conditions, not variables and not data If temperature is a variable in the experiment, it can have a column If all experiments were done at the same temperature, this information should be noted in Materials and Methods The data can easily be presented in text: “Aeration of the growth medium was essential for the frowth of Streptomycetes coelicolor. At room temperature (24C), no growth was evident in stationary (unaerated) cultures, whereas substantial growth (OD, 78 Klett units) occurred in aerated cultures.” Useless table

Has no columns of identical readings and looks like a good table The independent variable column (temp) looks reasonable enough The dependent variable (growth) has a suspicious number of zero You should question any table with large number of zero or a large number of 100s when percentage are used Useless table

“ The oak seedlings grew at temperatures below 20°C and 40°C; no measurable growth occurred at temperature below 20°C or above 40°C.” Useless table

“S. griseus, S. coelicolor, S. everycolor, and S. rainbowensky grew under aerobic conditions, whereas S. nocolor and S. greenicus required anaerobic onditions.” Useless table

Not all numerical data must be put in a table “The difference between the failure rates – 14% (5 of 35) for nocillin and 26% (9 of 34) for potassium penicillin V – was not significant (P=0.21).” Useless table

Tips In presenting numbers, give only significant figures. Nonsignificant figures may mislead the reader by creating a false sense of precision. Unessential data, such as laboratory numbers, results of simple calculations, and columns that show no significant variations, should be omitted. Present the data in the text, or in a table, or in a figure. Never present the same data in text, or in a table, or in a figure However, selected data can be singled out for discussion in the text.

How to arrange the data? The data can be presented either horizontally or vertically But “can” does not mean “should”; the data should be organized so that the like elements read “down”, not across

Read down Read across

Well-construct table Its read down, not across. It has headings that are clear enough to make the meaning of the data understandable without reference to the text. It has explanatory footnotes, but they do not repeat excessive experimental detail. Note the distinction. It is proper to provide enough information so that the meaning of the data is apparent without reference to the text, but it is improper to provide in the table the experimental detail that would be required to repeat the experiment.

Exponents in table heading If possible, avoid using exponents in heading table Why – to avoid confusion ‘cpm x 10 3 ’ and ‘cpm x refer to thousands of counts per minute If it is not possible to avoid, if may be worthwhile to state in a footnote (or in figure legend), in words that eliminate the ambiguity

Titles, footnotes, and abbreviations Title of a table (or legend of a figure) is like the title of your thesis. Should be concise and not divided into two or more clauses or sentences. Unnecessary words should be omitted. Give careful thought to the footnotes to the tables. If abbreviations must be define, you should list the abbreviations used in abbreviations list.

How to Prepare Effective Graphs

When to use graphs Graphs are very similar to tables as a means of presenting data in an organized way In fact, the results of many experiment either as tables or as graphs How to decide which is preferable? – difficult decision A good rules might be: if the data show pronounced trends, making an interesting picture, use a graph If the numbers just sit there, with no exciting trend in evidence, a table should be satisfactory Tables are also preferred for presenting exact numbers

Figure 1 could be replace by one sentences in the text “Among the test group of 56 patients who were hospitalized for an average of 14 days, 6 acquired infections.”

Compare Table 9 and Figure 2

Figure 2 clearly seems superior to Table 9 In the figure, the synergistic action of the two-drug combination is immediately apparent The reader can quickly grasp the significance of the data It also appears from the graph that streptomycin is more effective than is isoniazid, although its action somewhat slower; this aspect of the results is not readily apparent from the table

Example of a nice graph The lettering was large enough It is boxed, rather than two- sided (compare with Figure 2), making it a bit easier to estimate the values on the right-hand side of the graph The scribe marks point inward rather than outward

Tips Be consistent from graph to graph If you are comparing interventions, keep using the same symbol for the same intervention Do not extend the ordinate or the abscissa beyond what the graph demands If your data points range between 0 and 78, your topmost index number should be 80. You might feel a tendency to extend the graph to 100, a nice round number (especially if the data points are percentages) Your reference numbers should be 0, 20, 40, 60, and 80.

Symbols and legends You must define the symbols in the figure legend You should use only those symbols that are considered standard and that are widely available (○, Δ, □, ●) Different types of connecting line (solid, dashed) can also be used But, do not use different types of connecting line and different symbols

Other type of graphs

How to Prepare Effective Photographs

Significant Clear High-quality Crop the features of special interest

What is missing in these three photograph?

DATA ANALYSIS

The purpose To answer the research questions and to help determine the trends and relationships among the variables.

STEPS IN DATA ANALYSIS BEFORE DATA COLLECTION Determine the method of data analysis Determine how to process the data Prepare dummy tables AFTER DATA COLLECTION Process the data Prepare tables and graphs Analyze and interpret findings Consult supervisor/plv/etc Prepare for editing & presentation

KINDS OF DATA ANALYSIS 1. Descriptive analysis Refers to the description of the data from a particular sample; hence the conclusion must refer only to the sample. In other words, these summarize the data and describe sample characteristics. 2. Inferential analysis The use of statistical tests, either to test for significant relationships among variables or to find statistical support for the hypotheses.

Classification of Descriptive Analysis 1. Frequency Distribution A systematic arrangement of numeric values from the lowest to the highest or vice versa. 2.Measure of Central Tendency Average of the set values (mode, median, mean) 3. Measure of Variability Statistics that concern the degree to which the scores in a distribution are different from or similar to each other. (range, standard deviation)

Inferential Analysis The use of statistical tests, either to test for significant relationships among variables or to find statistical support for the hypotheses. Inferential Statistics ANOVA (significant of differences between means of two or more groups) T-test Hypothesis The outcome of the study perhaps may retain, revise or reject the hypothesis and this determines the acceptability of hypotheses and the theory from which it was derived.

INTERPRETATION OF FINDINGS/RESULTS, IMPLICATIONS AND INFERENCES Sufficient data should be used to justify your inferences or generalization. The implications suggested by the data should be explained and discussed thoroughly in this portion of your thesis. The data analysis involves comparing values on the dependent measures in statistical cases. In the non statistical approach, these comparisons usually involve visual inspection of data. Evaluation depends on projecting from baseline data what findings would be like in the future if some variables were not experimented.

How to write the discussion

7 key questions to write discussion 1.Does my data agree/disagree with other? 2.Can other people data/hypothesis help to explain my data? 3.Does your data help to explain other people data? 4.What assumption have you made in doing the work and what would change if you change them? 5.Are there alternative theories as to why the response you observed occurred? 6.What further knowledge is required for the field (especially for thesis)? 7.What impact does this work have on industry?

Any question?

Thank you