I recently purchased a bird feeder to attract more birds, so I tested different bird seeds from a local store. The store sells three different bird seed varieties: low-cost, moderate-cost, and high-cost varieties. According to the store seller, this means that each type of seed has a different potential to attract birds. However, I wondered whether the seeds exhibited varied capabilities, so I set out to test this analogy using a scientific method (Applying the Scientific Method).
The observation’s question informs a research hypothesis: the low-cost, moderate-cost, and high-cost seed varieties exhibit varied abilities to attract birds (Applying the Scientific Method).
- a bird feeder
- low-cost seeds
- medium-cost seeds
- high-cost seeds
- corn grains
- open space
The subjects included in the experiment are the birds and me as the researcher. Moreover, the number of birds attracted is the dependent variable. Consequently, it depends on the type of seeds provided, usually the independent variable. Therefore, for this experiment, the sample size constitute four bird feeders: feeder one having low-cost seeds, feeder two having medium-cost seeds, and feeder three having high-cost seeds. Consequently, the fourth feeder defines as a control experiment filled with non-commercial feeds; and corn grains. Hence, the comparisons made from the experiment will help us determine whether a different variety of seed grain affects the number of birds attracted (Applying the Scientific Method).
A control group is a vital aspect of a research experiment as it allows the researcher to test the hypothesis against already established variables and assumptions. Hence, scientific test done under controlled conditions, meaning that specific factors changes at a time while others remain constant (McKubre et al., 2018) (Applying the Scientific Method).
In this experiment, the dependent variable termed as the number of birds affected by different seed varieties’ independent variables. However, there is no definite way to test a hypothesis using controlled groups in most cases. Therefore, researchers make predictions about patterns seen in nature if the patterns exist. For this case, a control group of ordinary grains: it has no modifications to the commercial bird feed.
An experimental design uses a scientific approach where one or more independent variables, seed varieties, gets manipulated and applied to dependent variables: birds, to measure their effectiveness. Moreover, the measuring of effects of independent variables on dependent variables takes place over time to allow researchers draw meaningful and reasonable relationships between variables (Applying the Scientific Method).
In this experiment, a true experimental research design is essential. The design is justifiable: the study has a control group, a variable that I could manipulate, and a random distribution. Therefore, I will measure the effects of three seed varieties and a control group on the number of birds attracted and determine their relationship. Since this experiment is based on observation, data gathering mainly done by observing patterns and variations in the number of birds attracted by different seed varieties. I intend to observe and record findings using photographic observations, field notes, and datasheets.
Data Collection (Applying the Scientific Method)
Data collection refers to the process of gathering and analyzing information on the variables that enable a person to respond to the research questions, test a hypothesis, and assess the outcomes. The process begins by looking at each research question and developing the best way to collect data and find ways to respond to the questions (McKubre et al., 2018). As stated above, this experiment would utilize photographic observations and field notes. The focus would be capturing images of the number of birds at each feeder at one particular time (Applying the Scientific Method).
The feeders will be put in an open space at intervals, one at a time. Photographs taken show significant disparities depending on the type of seeds placed in a bird feeder. The other data collection tool uses field notes: they allow the recording of time intervals, frequency, and rates. Datasheets also used to input numerical data. Moreover, taking field notes enables a researcher to critically analyze every event as it unfolds. Hence, in this case, upon replacing the seeds. I noted that every data collection method yields different results and depicts the efficiency of a particular research tool provided it remains suited for that study (Applying the Scientific Method).
Data analysis refers to how data is cleared, transformed, and modeled to find useful information necessary during the decision-making process. Moreover, data analysis performs the duty of extracting useful information from given or selected data with selective choices (Kim & Wang, 2019). Consequently, in this experiment, I would analyze data obtained from photographic observations, datasheets, and field notes. Through critical analysis, one interprets results by following critical steps to apply the results (Applying the Scientific Method).
In this experiment, data will be gathered in photographs, numerical data, and notes. After analyzing data, results presented as frequencies and time intervals will be presented in graphical representation using charts and graphs. Photographs will be integrated into the final results as a visual reference to depict actual events in the experiment.
Data collection tools used in any experiment present useful findings and results. I established that high-cost seeds attracted many birds compared to both low-cost and medium-cost seeds. In contrast, corn grains attracted half the number of birds attracted by high-cost seeds. These disparities support my hypothesis that low-cost, moderate-cost, and high-cost seed varieties exhibit varied abilities to attract birds. The hypothesis would be rejected should all feeds attract the same number of birds (Applying the Scientific Method).
Kim, J. K., & Wang, Z. (2019). Sampling techniques for big data analysis. International Statistical Review, 87, S177-S191.
McKubre, M. C., Macdonald, D. D., Sayers, B., & Macdonald, J. R. (2018). Measuring techniques and data analysis. Impedance Spectroscopy: Theory, Experiment, and Applications, 107-174.