Information

How would you identify the independent variable in an experiment where it isn't specified?


I know that the above question is broad, but I specifically want an answer in terms of the following investigation - all of question 3 (ONLY) of this paper.

The independent variable, according to this mark scheme is the presence of hidden food in the cage to which the rats are released. How am I to identify this if it's not stated that the investigation investigates the ability to learn with the presence of hidden food.


Dependent and Independent Variables

In analytical health research there are generally two types of variables. Independent variables are what we expect will influence dependent variables. A Dependent variable is what happens as a result of the independent variable. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in children, vehicle exhaust is the independent variable while asthma is the dependent variable.

A confounding variable, or confounder, affects the relationship between the independent and dependent variables. A confounding variable in the example of car exhaust and asthma would be differential exposure to other factors that increase respiratory issues, like cigarette smoke or particulates from factories. Because it would be unethical to expose a randomized group of people to high levels of vehicle exhaust, [1] a study comparing two populations with differential exposure to vehicle exhaust would rely on a natural experiment, or a situation in which this already occurs due to factors unrelated to the researchers. In this natural experiment, a community living near higher concentrations of car exhaust may also live near factories that pollute or have higher rates of smoking.

When running a study or analyzing statistics, researchers try to remove or account for as many of the confounding variables as possible in their study design or analysis. Confounding variables lead to bias, or a factor that may cause an estimate to differ from the true population value. Bias is a systematic error in study design, subject recruitment, data collection, or analysis that results in a mistaken estimate of the true population parameter. [2]

Although there are many types of bias, two common types are selection bias and information bias. Selection bias occurs when the procedures used to select subjects and others factors that influence participation in the study produce a result that is different from what would have been obtained if all members of the target population were included in the study. [2] For example, an online website that rates the quality of primary care physicians based on patients&rsquo input may produce ratings that suffer from selection bias. This is because individuals that had a particularly bad (or good) experience with the physician may be more likely to go to the website and provide a rating.

Information bias refers to a &ldquosystematic error due to inaccurate measurement or classification of disease, exposure, or other variables.&rdquo [3] Recall bias, a type of information bias, occurs when study participants do not remember the information they report accurately or completely. The subject of confounding and bias relates to a larger discussion of the relationship between correlation and causation. Although two variables may be correlated, this does not imply that there is a causal relationship between them.

One way to determine whether a relationship between variables is causal is based on three criteria for research design: temporal precedence meaning that the hypothesized cause happens before the measured effect covariation of the cause and effect meaning that there is an established relationship between the two variables regardless of causation and a lack of plausible alternative explanations. Plausible alternative explanations are other factors that may cause the dependent variable under observation. [4] . These alternative explanations are closely related to the concept of internal validity.


What are experimental variables?

Learn about the different types of variables in an experiment.

Variables are an important part of an eye tracking experiment. A variable is anything that can change or be changed. In other words, it is any factor that can be manipulated, controlled for, or measured in an experiment.
Experiments contain different types of variables below, we will present you with some of the main types and thier definitions, then finish by giving an example containing all variable types.

Types of experimental variables:

  • Independent variables (IV): These are the factors or conditions that you manipulate in an experiment. Your hypothesis is that this variable causes a direct effect on the dependent variable.
  • Dependent variables (DV): These are the factor that you observe or measure. As you vary your independent variable you watch what happens to your dependent variable.

Figure 1. Shows the relationship between the independent and dependent variable.

  • Extraneous variable: An extraneous varable is any extra factor that may influence the outcome of an experiment, even though it is not the focus of the experiment. Ideally, these variables won&rsquot affect the conclusions drawn from the results as a careful experimental design should equally spread influence across your test conditions and stimuli. Nevertheless, extraneous variables should always be considered and controlled when possible as they may introduce unwanted variation in your data. In this case, you need to tweak your design and procedure to be able to keep the variation constant or find a strategy to monitor its influence (constant or controlled variables). All experiments have extraneous variables. Here are some examples of different types of extraneous variables:
    • aspects of the environment where the data collection will take place, e.g., room temperature, background noise level, light levels
    • differences in participant characteristics (participant variables) and
    • test operator, or experimenter behavior during the test, i.e., their instructions to the test participants are not consistent or they give unintentional clues of the goal of the experiment to the participants.

    Figure 2. Displays the effect of extraneous variables on the relationship between the independent and dependent variables.

    • Controlled (or constant) variables: Are extraneous variables that you manage to keep constant or controlled for during the course of the experiment, as they may have an effect on your dependent variables as well.
    • Participant variables: Participant variables can be defined as the differing individual characteristics that may impact how a participant responds in an experiment. Examples of participant variables include gender, age, ethnicity, socioeconomic status, literacy status, mood, clinical diagnosis etc.
    • Stimulus variables: These are specific features of your stimulus or group of stimuli that are part of the context in which the behavior occurs. These are often an expression of or a subset of your independent variables and covariates. Examples include the number of items, item category, stimulus crowdedness, color, brightness, contrast, etc.

    Operationalizing variables

    Before you start your experiment, you need to have a clear definition of, and strategy for, how each variable will be measured and recorded. This process is called variable operationalization.

    For example, you are interested in studying attitudes towards food, visual attention, and food choice. In your first study, your objective is to investigate &ldquothe effect of personal health goals on visual attention to different food groups&rdquo.


    Extraneous and Confounding Variables

    It is important to note that the independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

    For example, in our previous description of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so this impact on the results can be controlled for.

    There are two basic types of extraneous variables:

    • Participant Variables: These extraneous variables are related to individual characteristics of each participant that may impact how he or she responds. These factors can include background differences, mood, anxiety, intelligence, awareness and other characteristics that are unique to each person.
    • Situational Variables: These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

    Other extraneous variables include the following:

    • Demand characteristics: Clues in the environment that suggest how a participant should behave
    • Experimenter effects: When a researcher unintentionally suggests clues for how a participant should behave

    In many cases, extraneous variables are controlled for by the experimenter. In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors do not interfere with the results.

    Confounding Variables

    If a variable cannot be controlled for, it becomes what is known as a confounding variable. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable or an interaction of the two.


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    Calculate total variable cost by multiplying the cost to make one unit of your product by the number of products you’ve developed. For example, if it costs $60 to make one unit of your product, and you’ve made 20 units, your total variable cost is $60 x 20, or $1,200.

    In mathematics, a variable is a symbol or letter, such as “x” or “y,” that represents a value. For example, in the equation below, y is the “dependent variable” because its value is based on the value assigned to the “independent variable” x. …


    Categorical and Continuous Variables

    Categorical variables are also known as discrete or qualitative variables. Categorical variables can be further categorized as either nominal, ordinal or dichotomous.

    • Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the USA by state. In this case there will be many more levels of the nominal variable (50 in fact).
    • Dichotomous variables are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female". This is an example of a dichotomous variable (and also a nominal variable). Another example might be if we asked a person if they owned a mobile phone. Here, we may categorise mobile phone ownership as either "Yes" or "No". In the real estate agent example, if type of property had been classified as either residential or commercial then "type of property" would be a dichotomous variable.
    • Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. So if you asked someone if they liked the policies of the Democratic Party and they could answer either "Not very much", "They are OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories, namely "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the levels, we cannot place a "value" to them we cannot say that "They are OK" is twice as positive as "Not very much" for example.

    Continuous variables are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables.

    • Interval variables are variables for which their central characteristic is that they can be measured along a continuum and they have a numerical value (for example, temperature measured in degrees Celsius or Fahrenheit). So the difference between 20°C and 30°C is the same as 30°C to 40°C. However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable.
    • Ratio variables are interval variables, but with the added condition that 0 (zero) of the measurement indicates that there is none of that variable. So, temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0°C does not mean there is no temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often called absolute zero) indicates that there is no temperature whatsoever. Other examples of ratio variables include height, mass, distance and many more. The name "ratio" reflects the fact that you can use the ratio of measurements. So, for example, a distance of ten metres is twice the distance of 5 metres.

    • Independent Variable: The independent variable is the one condition that you change in an experiment.
      Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature.
    • Dependent Variable: The dependent variable is the variable that you measure or observe. The dependent variable gets its name because it is the factor that is dependenton the state of the independent variable.
      Example: In the experiment measuring the effect of temperature on solubility, solubility would be the dependent variable.
    • Controlled Variable: A controlled variable or constant variable is a variable that does not change during an experiment.
      Example: In the experiment measuring the effect of temperature on solubility, controlled variable could include the source of water used in the experiment, the size and type of containers used to mix chemicals, and the amount of mixing time allowed for each solution.
    • Extraneous Variables: Extraneous variables are "extra" variables that may influence the outcome of an experiment but aren't taken into account during measurement. Ideally, these variables won't impact the final conclusion drawn by the experiment, but they may introduce error into scientific results. If you are aware of any extraneous variables, you should enter them in your lab notebook. Examples of extraneous variables include accidents, factors you either can't control or can't measure, and factors you consider unimportant. Every experiment has extraneous variables.
      Example: You are conducting an experiment to see which paper airplane design flies longest. You may consider the color of the paper to be an extraneous variable. You note in your lab book that different colors of papers were used. Ideally, this variable does not affect your outcome.

    In a science experiment, only one variable is changed at a time (the independent variable) to test how this changes the dependent variable. The researcher may measure other factors that either remain constant or change during the course of the experiment but are not believed to affect its outcome. These are controlled variables. Any other factors that might be changed if someone else conducted the experiment but seemed unimportant should also be noted. Also, any accidents that occur should be recorded. These are extraneous variables.


    Software Activity

    The types of variables you are analyzing directly relate to the available descriptive and inferential statistical methods.

    • assess how you will measure the effect of interest and
    • know how this determines the statistical methods you can use.

    As we proceed in this course, we will continually emphasize the types of variables that are appropriate for each method we discuss.


    Two rubrics will be used to assess student lab report (sample of report included below) and student participation.
    A written exam will be used to determine if a student has met the goals of the activity.

    Example of student report (some areas are condensed down here to save space)
    Student name ____________
    Rate of Photosynthesis Research


    Background: Where in a leaf does photosynthesis mainly occur? How does carbon dioxide get into a leaf? Where / how does oxygen leave a leaf? How does water get into a leaf from the roots?

    Obtain a prepared slide of a leaf cross section (x-section). Using 100x make a sketch of
    what you see.

    Use the text book or internet to label the following tissues
    Upper epidermis, lower epidermis, palisade mesophyll, spongy mesophyll,
    vein (label both xylem and phloem), guard cells, stoma

    1) In which of the labeled structures, does most of the photosynthesis occur? (hint
    there are more chloroplasts here)

    2) Through what structure does carbon dioxide get into the leaves so photosynthesis can occur?

    3) What is the function of the guard cells?

    4) Of what purpose does the spongy mesophyll serve to the leaf and the process of photosynthesis?


    5) Through which of the labeled structures does water get to the leaves from the roots?


    6) Through which of the labeled structures are sugars, that are made during photosynthesis,
    transported to other parts of the plant where they can be used for energy or stored?


    7) discuss the variations / adaptations that desert plants, water plants, and plants that grow
    well in shade have in their leaves that allow them to survive in their particular environments.

    Write out a balanced equation for photosynthesis:

    Experimental Design
    Your table will design an experiment to test how a selected variable affects the rate of photosynthesis. Follow the information below to make "sinking plant disks". You will measure how long it takes (in seconds) for the disks to float as a way to measure the rate of photosynthesis.

    Preparation of the leaf disks:
    1) Use the cork borer (to cut out the number of disks needed for your experiment).
    2) Put disks in a syringe and suck up 5 cc (5 ml) of .2% sodium bicarbonate (baking soda)
    3) Put finger over end of syringe, pull back on plunger to about the 35 cc mark (on a 60 cc syringe) and hold this position for 30 seconds. You should see air coming out the sides of the disks. As this is done, the oxygen is being removed from the spongy layer of the leaf and the .2% sodium bicarbonate is entering the spongy layer. This is the source of carbon dioxide needed for the plant to carry out photosynthesis
    4) Carefully squirt out the .2% sodium bicarbonate. Suck up about 10 cc's of water. Check to see if the plant disks sink in the water. If they don't, remove the water and try steps 2 and 3 again.
    5) Choose the disks that sink. Make sure enough disks are available to properly complete a controlled experiment. They are now ready to be used in your experimental set up. The disks will float when they have produced a measured amount of oxygen through photosynthesis. The time needed for the disks to float is an indirect measure
    of the rate of photosynthesis occurring in the leaf disks.

    Descriptive title of experiment:

    The effect of ________________________________________________ on
    the rate of photosynthesis.

    Hypothesis (use if/ then format)

    Explain the logic of the stated hypothesis


    Sketch of the experimental design used. (the sketch should be specific enough so that the experiment could be reproduced exactly as it was set up include all measurements, angles, label materials / solutions used, wattage and type of light bulbs, etc.)

    Is the data collected qualitative or quantitative data? discuss

    The independent variable (manipulated) variable in the experiment is

    The dependent variable (responding) in the experiment is


    How was the experiment controlled?


    Data chart: (you design, label and fill in with data)(you must have enough data to make a graph)

    Graph of data (obtain a piece of graph paper, make appropriate graph that has a title and is properly labeled, attach graph to this lab report)

    Results / discussion / analysis of data:


    Findings/Conclusion/ list possible sources of error

    Application to World environmental issues: (list ideas generated during brainstorming session)