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APES Guidelines for Experimental Research p. 1 Laboratory Report Format Refer to the Experimental Organizer/Design Outline to support completion of the Laboratory Report. A. Title: Use the “The effect of IV on the DV” format. B. Introduction/Purpose: Use the following questions to write the introduction (one paragraph per bullet): • WHAT background information does the reader needs to understand and appreciate the experiment (consider IV and DV)? Cite references in the body of the text [i.e (Author, Date)] and include each source in the Bibliography. • WHY is this experiment being done (purpose)? Why is the experiment important; who might be interested in the results? • HOW do you predict the experiment will turn out? Explain the rationale behind the Hypothesis statement. Cite references in the body of the text [i.e (Author, Date)] and include each source in the Bibliography. C. Hypothesis: Use “If IV then DV” format. Be specific: the hypothesis should be able to be clearly supported or refuted by your experiment. D. Materials and Procedures: 1. List all materials used. 2. In sequential order, list the experimental set-up procedures. 3. In sequential order, list the data-collection procedures. 4. Identify the independent variable (IV), levels/treatments of the IV, number of trials for each level, quantitative and qualitative dependent variables (DV), control and constants of the experiment. E. Data/Results: Data Tables: Include quantitative and qualitative data tables with all variables, trials, and statistics. Graphs: Use appropriate graphs with titles and labeled axes. Laboratory Notebook: Include if appropriate. F. Discussion: Use the following questions to write the Discussion (one paragraph per bullet): • WHAT happened? Describe the data collected for each IV level/treatment. Make references to actual data values and graphs. Discuss data reliability (refer to standard deviation, when available). Identify patterns/trends and unexpected results (anomalies) in the data. • WHY did you get the results you did? Compare your findings to other research (in class or literature) and propose explanations for discrepancies. Cite references in the body of the text [i.e (Author, Date)] and include each source in the Bibliography. • HOW could you improve and further the experiment? Make suggestions for design and/or procedural improvements and make recommendations for further study. G. Conclusion: Briefly state major findings of the experiment in reference to the experimental purpose. Was the Hypothesis supported or refuted? H. Bibliography/References Cited: Follow the standard format. APES Guidelines for Experimental Research p. 2 Rules For Reference Citations And Bibliographies (American Psychological Association Guidelines )The References Cited section is a bibliography of sources used in producing your report. It usually appears at the end on a separate page, alphabetically by main entry (e.g., author or other first component). Use the formatting guidelines depending on the source type you used. Formatting guidelines are also available at: http://www.english.uiuc.edu/cws/wworkshop/writer_resources/citation_styles/apa/apa.htm. Bibliographies: Book: no named author: Title. (date). Place: Publisher. Book: one author: Lastname, Initial. (date). Title. Place: Publisher. Book: same name after first entry: ___. (date). Title. Place: Publisher. Book: with editor: Lastname, Initial. (Ed.) (date).Title. Place: Publisher. Book: with translator: OriginatorLastname, Initial. (date). Title. (Initial Lastname, Trans.) Place: Publisher. Book: corporate author: CorporateAuthor. (date). Title. Place: Publisher. Book: two (through six) authors: 1stLastname, Initial, NextLastname, Initial, & EndLastname, Initial. (date). Title. Place: Publisher. Book: more than six authors: Lastname, Initial, et al. (date). Title. Place: Publisher. Work in an anthology: Lastname, Initial. (date). Selection. In Initial Lastname (Ed.) Title. (pp. x-xx) Place: Publisher. Article in a reference work: Lastname, Initial. {if known} (date). Selection. In Initial Lastname (Ed.), Title (Vol. x, pp. x-xx). Place: Publisher. Periodical article: Lastname, Initial (year, Month day). Article. JournalTitle, pp.x-xx. or Lastname, Initial (year, Month day). Article. JournalTitle xx (issue#), x-xx {pages}. Government publication: GovernmentAgency. (date). Title. Place: Publisher. Map or chart: Title, [Medium]. (date). Place: Publisher. Speech: Lastname, Initial. (year, Month day). Speechtitle. In Meetingtitle. Place: SponsoringOrganization. APES Guidelines for Experimental Research p. 3 Interview: Lastname, Initial, Personal interview, Month day year. Correspondence: Lastname, Initial. (date). [Letter to Initial Lastname]. Mass media: Lastname, Initial (Producer), {if known} & Lastname (Director). {if known} Title. [Medium]. Place: Distributor. Recording: Lastname, Initial (Function). (date). Recordtitle. [Medium} Available: Vendor, Place. Database source: Lastname, Initial. {if known} (date). PublicationInfo. DatabaseTitle. [Medium]. Available: Vendor, Place. CD-ROM source: Article. (date). ProductTitle. [Medium]. Place: Publisher. Electronic source: Lastname, Initial. (Dateofwork). Title. [Online]. Available email/ftp/telnet/http:address [dateofaccess]. In-text citations: Sources are acknowledged in the body of the text as follows: ... text (Lastname, date). Two authors (non-quote): (1stLastname and 2dLastname, date) More than two authors (non-quote): [note: the first time, cite all names] (1stLastname, NextLastname, and EndLastname, date) then (1stLastname, et al, date) One or two authors (direct quote): (lstLastname and 2dLastname, date, p. x) More than two authors (direct quote): [note: the first time, cite all names] (1stLastname, NextLastname, and EndLastname, date, p. x) then (1stLastname, et al, date, p. x) APES Guidelines for Experimental Research p. 4 Experimental Organizer Guidelines All experiments have certain components in common. Throughout the year we will be using what is called a “Experimental Organizer” to organize and identify the parts of any scientific experiment. This handout will provide you with useful definitions and a completed example of a Experimental Organizer, to guide you in using this type of outline. Note: This is different than the Experimental Design (Procedures), section D, of the Laboratory Report Format. The Experimental Organizer is a tool for organizing an experiment and writing the Experimental Design (Procedures). Experimental Organizer Definitions Title (Purpose): a statement that reflects the variables being tested in the experiment. Often written as The Effects of …(IV)… on the …(DV) Hypothesis: a prediction about the relationship between the variables that can be tested. Often written as “if”, “then” statement. Independent Variable (IV): the variable that is tested (i.e. the one that is purposefully changed; “cause”). Category of Independent Variable: there are two categories of independent variables. continuous – levels of the independent variable that are measurements based on a continuous scale (generally graphed using a line graph). discontinuous – levels of the independent variable that are discrete categories (generally graphed using a bar graph). Control: the standard for comparing experimental effects. Levels of the Independent Variable (Treatments): the modifications of the independent variable for comparing the experimental effects to the control. Repeated Trials: the number of experimental repetitions, objects, or organisms tested at each level of the independent variable. Dependent Variable (DV): the variable that is measured (i.e. the data collected; “effect”). Category of Dependent Variable: there are two major categories of data (use both when possible). quantitative - measurements based on a continuous scale (numerical) qualitative - counts of discrete or discontinuous categories Constants (C): all factors that remain the same throughout the experiment. Experimental Organizer Example Consider the following scenario: After studying about recycling, members of John’s biology class investigated the effect of various recycled products on plant height. Because decomposition is necessary for release of nutrients, the group hypothesized that the type of compost, grass or food, would affect the height of bean plants. Three flats of bean plants (25 plants/flat) were grown for 5 days. Then the plants were fertilized as follows: (a) Flat A: 450 g of grass compost, (b) Flat B: 450 g of food compost, and (c) Flat C: no compost. The plants received the same amount of sunlight and water each day. At the end of 30 days the group recorded the height of the plants (cm). Title/Purpose (Describe the experiment by using the format, “The effect of ___ on ___”) The Effect of Different Types of Compost on Bean Plant Height. Hypothesis (Make specific predictions about the experimental outcome using the format, “If…, then…”) If grass compost is applied to the bean plants, then the plants will grow taller than plants grown with food compost or no compost. Independent Variable I.V. (What you will be testing): Type of Compost Category of I.V.: (Identify if the I.V. is continuous or discontinuous.): Discontinuous Levels of I.V. (Treatments) (2 or more plus the control, which must be identified) Grass compost Food compost Number of Trials (# of times each level of I.V. is tested) 25 plants 25 plants No compost (Control) 25 plants Dependent Variable (What you are measuring. Include all appropriate units): Height of plants (cm) APES Guidelines for Experimental Research p. 5 Category of Data (Identify if the data is qualitative and/or quantitative): Quantitative Constants (List all conditions which are the same for each trial): Amount of light, amount of water, amount of compost APES Guidelines for Experimental Research p. 6 Measuring & Calculating Data Guidelines Metric System The metric system is the most commonly used system of measurement in the scientific community. The units for measuring length, volume, temperature and mass are given in the table below. Length Volume Temperature Mass 0.0° Celcius (C°) = 1.00 meter (m) = 1.00 liter (L) = 1.00 gram = freezing point of water 0.001 kilometer (km) 1,000.0 milliliters (mL) = 0.001 kilogram (kg) = 100.0°C = 100.0 centimeters (cm)= 3 1,000.0 milligrams (mg) 1000.0 cubic centimeters (cm ) boiling point of water 1,000.0 millimeters (mm)= 1,000,000.0 micrometers (um) Precision of Measurements The measurement tool you are using determines the precision of your measurement. You can only be as precise as the tool you use! Estimate to the nearest half unit shown on your measurement tool. Calculating with Precision When calculating with measurement values, remember this basic rule: Your answer should be given to the same degree of precision as the least precise of the values used in your calculation. We use this simplified rule to make sure we use "significant figures". EXAMPLES: a) 1.0 ÷ 3.0 = .3 (NOT .3333333333333) –accurate to the tenths place (round to the nearest tenth) b) 4567.62 x 50 = 228380 –accurate to the tens place (round to the nearest ten) Metric Conversions The metric system is based on powers of ten. Conversions between the different units within the metric system is accomplished by dividing or multiplying by a factor of 10. This involves moving the decimal point the appropriate number of places in the appropriate direction. The quick conversion line below can assist you. Each vertical line represents a tens place: Number of units= 1000 100 10 1 .1 .01 .001 kilo- hecto- deka- UNIT: deci-centi- millimeters Take away zeros liters Add zeros grams =Number of units micro- To remember the prefixes on the "Quick Conversion Line” use the following statement: Kids Having Donuts Usually Drink Chocolate Milk. English Conversions The English system is used exclusively in the United States. Unlike the metric system, the English system is not based on tens. Conversions between units require using conversion factors and are therefore much more difficult. As long as the English system continues to be used, conversions between the two systems will be necessary. Use the conversion factors below to complete the problems that follow. Length Volume Mass 2.54 cm = 1.0 inch 1.0 liter = 1.06 quarts 1.0 meter = 3.28 feet 3.79 liters = 1 gallon (U.S. liquid) 1.0 meter = 1.094 yards 1.609 kilometer = 1.0 mile 1.0 kilogram = 2.20 pounds APES Guidelines for Experimental Research p. 7 Data Analysis Guidelines MEASURES OF CENTRAL TENDENCY Mean Generally, the mean is the best measure of central tendency for quantitative data and should be used unless there are outliers that would distort the mean value. To calculate mean: 1. Add all data values in the set 2. Divide the sum of the data values by the number of data in the set, this value is the mean. 3. Record this value in the appropriate column of the data table. Mean = Sum of the data Number of data in the set Summary: Mean is the average of the data set. Median Median is an appropriate measure of central tendency for quantitative data that is “skewed” and for qualitative data with ranked categories. The median is the data value that falls in the exact middle of a data set that is ordered from smallest to largest. To determine the median: 1. List all data values in numerical order from least to greatest. 2. If the number of data in the set is odd, find the value that falls in the exact middle of the data set, this value is the median. 3. If the number of data in the set is even, calculate the mean of the two middle data values, this value is the median. 4. Record this value in the appropriate column of the data table. 1, 4, 4, 7, 8, 8, 9, 10, 42 Median = 8 1, 7, 8, 8, 10, 10, 42 , 52 Median = 8+10 = 9 2 Summary: Median is the middle number in an ordered data set. Mode Mode is an appropriate measure of central tendency for qualitative non-ranked data. The mode of a data set is the data value that occurs most frequently. If no data value in a data set occurs more than once, then we say that the data set has no mode. If two or more data value appear the most, then both values are considered the mode. To determine mode: 1. Look over the data set and count how many times each data value occurs. 2. Identify which data value occurs the most, this value is the mode. 3. Record this value in the appropriate column of the data table. white, white, red, brown, brown, red, white, white Mode = white APES Guidelines for Experimental Research p. 8 Summary: Mode is the most common data value in a data set. APES Guidelines for Experimental Research p. 9 MEASURES OF VARIATION Standard Deviation Standard deviation is a calculated value that describes the variation (or spread) of values in a data set. It is calculated using a formula that compares each piece of data to the mean. It is useful to think of this number as a “plus or minus” value, where a larger standard deviation indicates that the data are spread further from the mean and thus the mean is less reliable. In general, a large value of standard deviation indicates less confidence in the data set and its mean, while a small value of standard deviations suggests greater confidence in the data and its mean. To calculate standard deviation: 1. Calculate the mean. 2. Calculate the difference between the mean and each data value. 3. Square each difference. 4. Add the squared values together. 5. Divide the sum by the total number of data in the set. 6. The square root of the value calculated in the previous step is the standard deviation. 7. Record standard deviation as a value in the appropriate column of the data table. Plant Biomass (g) Difference between Biomass and Mean (g) Squared Value of Difference 3 1 1 5 1 1 6 2 4 4 0 0 6 2 4 2 2 4 2 2 4 Mean: 4 Sum: 18 Sum/Total # of Data: 18/7 Square Root of Quotient: √2.5 Standard Deviation: 1.6 Summary: Standard deviation describes how far the majority of the data is from the mean. To calculate mean and standard deviation using a graphing calculator* (Texas Instruments): 1. 2. 3. 4. 5. Press the Stat key, then choose Edit and press Enter. Enter data values in list one (L1). Press the Stat key again, choose Calc, then choose 1-Var Stats and press Enter. Choose 2nd, then L1 (the number 1 key) and press Enter. The x value is the mean and the x value is the standard deviation. *Any scientific calculator can calculate mean and standard deviation. Refer to the instructional manual for your scientific calculator for specific instructions. APES Guidelines for Experimental Research p. 10 APES Guidelines for Experimental Research p. 11 To calculate standard deviation using Microsoft Excel 1. 2. 3. 4. Create a data table in an Excel spreadsheet complete with all relevant data values and columns headings for data analysis (ex. mean, std. dev) Highlight a cell where you want to add a calculation Choose “Insert” and then “Function…” from the menu Note: There is no “Function” to calculate frequency distribution. You will either need to determine frequency distribution and input the calculated value into the data table OR create a formula in Excel to calculate frequency distribution. Select the cells with the appropriate data to be analyzed Either select and highlight the cells, or type the cell numbers into the window that appears. Note: When calculating standard deviation, do not include the mean value when selecting the data to be analyzed. Soil type (IV) Potting Soil (Control) Marsh Mt. Tam Plant 1 Height in cm (DV) Plant 3 Plant 2 20.5 10.5 15.2 Mean 22.2 12.2 16.3 Std. Dev. (+/-) 25.1 9.5 14.2 22.6 10.7 15.3 2.3 1.4 1.1 Frequency Distribution When using qualitative data, frequency distribution is a good expression of variation. Frequency distribution is a decimal that represents the number of times a particular data value occurs for each experimental group. It is calculated by dividing the number of times a particular data value occurs by the total number of data in the set. To calculate frequency distribution: 1. Identify and the number of times a particular data value occurs in a data set. 2. Divide this number by the total number of data in the set. 3. Report this value as a decimal. 4. Calculate and record frequency distribution for all data categories in the appropriate column of the data table. To present frequency distribution in a data table: The Effect of the Amount of Compost on the Leaf Quality of Bean Plants Leaf Quality - 30 Days Trials Amount of Compost Median 1 2 3 4 5 Frequency Distribution 4 3 2 1 0.0 g 4 4 4 4 4 4 1.0 0 0 0 10.0 g 20.0 g 4 3 3 3 4 2 4 4 4 2 4 3 0.8 0.2 0.2 0.4 0 0.4 0 0 30.0 g 40.0 g 2 2 2 1 3 1 2 1 4 1 2 1 .2 0 0.2 0 0.6 0.2 0 0.8 50.0 g 2 3 3 3 2 3 0 0.6 0.4 0 Overall Plant Health 4 = green color, firm, no curled edges 3 = yellow-green color, firm, no curled edges 2 = yellow color, limp, with curled edges 1 = brown color, limp, with curled leaf APES Guidelines for Experimental Research p. 12 Graphing Guidelines Types of Graphs The type of graph used to display data depends largely on the independent variable. Category of Data for Graph Type IV continuous line graph discontinuous bar graph General rules for all graphs. Always use graph paper when graphing by hand. Use a ruler for all lines. Graph in pencil first, then trace over it in ink. All graphs should be titled by stating the relationship between the independent and dependent variables. Label each axis, with appropriate unit in parentheses. The x-axis represents the independent variable, while the yaxis represents the dependent variable. If multiple data sets are included on a graph, it must include a key. The Line/Scatter-Plot Graph Constructing a line graph or scatter-plot. • Draw and label the x and y axes of the graph. Indicate the appropriate units for each variable. • Determine an appropriate scale for each axis. Do this by finding the range of the values to be graphed (maximum - minimum), divide this range by the number of grids on the graph paper. To display the data most clearly, increments should increase using even values (i.e. 2, 4, 6, 8, not 1, 3, 5, 7). • Carefully plot the data. • Line Graph: Connect the points (straight line) starting with the first data point and ending with the last data point. Do not start at the origin unless you have that data (0,0). • Scatter-Plot: If interested in a linear relationship between the IV and DV, then draw a line of best-fit. The Bar Graph Constructing a bar graph. • Draw and label the x and y axes of the graph. Indicate the appropriate units for each variable. • Evenly space the x-axis with the levels (treatments) of the independent variable. Evenly distribute the values along the axis, leaving a space between each value. • Determine the appropriate scale for the y-axis, using the method described for line/scatter-plot graphs above. Subdivide the y-axis accordingly. • Draw a vertical bar from the value of the independent variable on the x-axis to the corresponding value of the dependent variable on the y-axis. Leave space between each bar. APES Guidelines for Experimental Research p. 13 Graphing Variation Plotting Standard Deviation (SD) Add the SD to each mean or median and draw a thin horizontal line above the point/bar on the graph. Subtract the SD from each mean or median and draw a thin horizontal line below the point/bar on the graph. Draw a thin vertical line to connect the two thin horizontal lines. See the diagram below. The Effect of Compost on Plant Height 25 20 Pla nt Height (cm ) 15 Mean Plant Heigh t 10 5 0 No Comp ost Grass Co mpos t Type of Compost Plotting Frequency Distribution (FD) A separate bar graph or pie chart can be used to show FD. Foo d Comp ost APES Guidelines for Experimental Research p. 14 The Effect of Compost on Plant Health 100% 90% Frequency Distribution 80% 70% 60% Unhealthy Healthy 50% 40% 30% 20% 10% 0% No Compost Grass Compost Food Compost Type of Compost Note on Graphing: Use common sense. Think about how to show your data to someone who has never seen it before. Make it as clear as possible. Computer Graphing Use the instructions below to enter, analyze, and graph data in Excel. Graphing in Excel: 1. Create a data table in an Excel spreadsheet complete with all relevant data values 2. Highlight data to appear on the y-axis (the DV). If appropriate, you can also highlight the data for the x-axis (the IV). Note: in many cases, this will be the values in the central tendency column 3. Select the chart wizard (from toolbar) or “Insert”…”Chart” from the menu Select the appropriate type of graph (recommended type for bar graphs is Clustered Column) and click ‘Next’. Click on the “Series” tab and change ‘series titles’ and ‘category x- axis labels’ if applicable. Note: ‘category x axis labels’ represent the levels of the independent variable Click on the grid icon next to the window labeled ‘category x-axis labels’ and then simply highlight the cells containing the levels of the IV on your data table. Click ‘Next’. Title the graph in the window labeled ‘Chart Title’ Label the x- axis by typing the independent variable into the window labeled ‘Category x- axis’. Don’t forget units! Label the y- axis by typing the dependent variable into the window labeled ‘Value y-axis’. Don’t forget units! 4. Click ‘Next’, and Finish as New Sheet Note: Graphs can often better express the difference between data values by changing the scale of the y-axis. To do this, double click on the y-axis of your graph. Under “scale”, change the minimum value when appropriate. Graphing Y Error Bars (standard deviation) in Excel: 1. If the graph is a bar graph, double click on one of the bars. If the graph is a line graph, double click on one of the data points in the graph. Note: This may not work if you created a 3 dimensional bar graph. Change the chart type to Clustered Column. 2. Choose tab titled “y error bars” from dialogue box 3. Click on the grid icon next to the window labeled Custom (+). Highlight the cells containing values for standard deviation on your data table. 4. Click on the grid icon next to the window labeled Custom (-). Highlight the cells containing values for standard deviation on your data table. 5. Click O.K. APES Guidelines for Experimental Research p. 15 Laboratory Report Grading Rubric CATEGORY INTRODUCTION 4 Experiment is supported by important, relevant and accurately cited background research on variables, purpose and predicted outcomes. Information is explained clearly, accurately and thoroughly using student’s own words. HYPOTHESIS Predicted relationship between variables and expected outcomes is clear and based on research. Materials are thoroughly identified. PROCEDURES RESULTS Procedures for both set-up and data collection are exceptionally detailed, clear and easy to follow. Experimental variables and constants are clearly identified. Raw data, statistical data and graphs relevant to hypothesis are accurate. Presentation is skilled and clear, with all necessary titles/labels. 3 Experiment is supported by relevant and cited background research on variables, purpose and predicted outcomes. Information is generally explained accurately using student’s own words. Predicted relationship between variables and expected outcomes is generally stated and based on research. Materials are generally identified. 2 Experiment is partially supported by relevant and/or cited background research on variables, purpose and/or predicted outcomes. Information explained with partial accuracy. 1 Experiment is not supported by background information on variables, purpose and/or predicted outcomes. Information is generally inaccurate and/or not clearly explained. Predicted outcomes are stated and partially on research. Predicted outcomes are unclear. Materials may be only partially identified. Materials are unclear or not identified. Procedures for both set-up and data collection are generally detailed and clear. Procedures for set-up and/or data collection may be partially complete. Procedures for set-up and/or data collection are unclear/not provided. Experimental variables and constants are generally identified. Experimental variables and constants may be partially identified. Experimental variables and constants are incorrect or not identified. Raw data, statistical data and graphs are generally relevant to hypothesis. Raw data, statistical data and graphs are partially relevant to hypothesis. Raw data, statistical data and graphs are incorrect, incomplete or not included. Presentation is generally clear, with necessary titles/labels. Clarity and accuracy of presentation is variable. Presentation is unclear and/or incomplete. APES Guidelines for Experimental Research p. 16 CATEGORY 4 3 2 1 Appropriate conclusions are supported by data and related to the hypothesis. Conclusions are generally supported by data and related to the hypothesis. Conclusions are partially supported by data and may be related to the hypothesis. Conclusions are unclear/incomplete and not supported by data. A clear, detailed explanation for why results occurred is supported by cited research. An explanation for why results occurred is generally supported by cited research. An explanation for why results occurred is partially supported by research. An explanation for why results occurred is unclear/incomplete or not included. Analysis of possible error, improvements and ideas for further study are insightful and creative. Analysis of possible error, improvements and ideas for further study are presented and are generally insightful. Analysis of possible error, improvements and ideas for further study may be present. Additional information reflecting what was learned from the experiment is clearly presented. Additional information reflecting what was learned from the experiment is generally presented CONCLUSION A clear, concise summary of the experiment’s major outcomes is clearly related to the hypothesis. A summary of the experiment’s major outcomes is generally related to the hypothesis. A summary of the experiment’s major outcomes may be related to the hypothesis. A summary of the experiment’s major outcomes is unclear/not included. BIBLIOGRAPHY Research includes multiple, reputable sources and is properly cited. Research includes multiple cited sources. Research includes cited sources. Research does not include cited sources. Written expression is partially clear and cohesive. Written expression is disorganized/unclear. Errors in grammar and spelling are noticeable. Errors in grammar and spelling are frequent. Aspects of the lab report may be partially completed or incorrectly formatted. Aspects of the lab report are incomplete and/or lacking proper formatting. DISCUSSION Written expression is clear, cohesive and detailed. WRITTEN EXPRESSION Errors in grammar and spelling are extremely rare. All aspects of the lab report are completed according to prescribed format. Written expression is generally clear and cohesive. Errors in grammar and spelling are rare. All aspects of the lab report are generally completed according to prescribed format. Additional information reflecting what was learned from the experiment may be partially presented. Analysis of possible error, improvements and ideas for further study are unclear/not included. Additional information reflecting what was learned from the experiment is unclear/not included. APES Guidelines for Experimental Research p. 17