independent
Dependent variable: Whether children were given to adopted parents or natural parents. Independent variable: social relationships at ages 8 and 16.
The independent variable is the variable that you are curious about, and that you are going to change is some systematic way in an experiment to see what affect your changes make. What you check, to see if there are differences, is the dependent variable. According to your hypothesis, the values of the dependent variable will 'depend' on how you manipulate the independent variable. You want to know the effect of growing plants under different colors of light. You want to know how different colors of light (the variable you will manipulate) will affect plant growth (the dependent variable). You will want to use several controls, too. For example, if you try the above but you use several different kinds of plant, of different ages, in different soils and temperatures and different amounts of water, and different lengths of exposure to light (some sunlight, some 'full-spectrum lamps', etc) your experiment will be without value, except as a lesson in how not to do it!
! ANOVA is generally computed for two or more QUANTITATIVE variables. If the quantitative variables are two or less in number, people prefer the t test (one sample t, paired t, or independent samples t) The Independent variable however is qualitative( for example, Girls and boys or Names of Schools.) It is the dependent variable that is Quantitative (for example, the ages - 2, 5 , 70, etc or weight or number of somethings). If you have 2 independent variables, you go for the two way ANOVA. Else, it's the one way ANOVA. !
The independent variable - if there is one. A variable that is common to a number of pairs of variables that you wish to compare. For example, if you want to compare height and mass at various ages, the age would be on the x-axis.
It depends what you mean. There are pitfalls. For instance, if you were to measure some other variable at various ages for the same individual then you would need to take that into account. There would be nothing wrong with using age as an independent variable though, as long as you correctly provided for the repeated measurements. You could also run into trouble if age were confounded with some other variable. For instance, in smokers the likelihood of finding lung cancer would increase with age but this might be because older smokers had, on average, been smoking for longer periods of time.
carbon dating
Research topic: The effect of caffeine on cognitive performance a. Dependent variable: Cognitive performance (measured through tasks like memory recall or reaction times) b. Independent variable: Caffeine consumption (dosing or presence/absence) c. Extraneous variable: Participant's sleep quality, time of day of testing Research topic: Impact of social media use on mental health a. Dependent variable: Mental health outcomes (measured through surveys or clinical assessments) b. Independent variable: Social media use (frequency, duration, or content) c. Extraneous variable: Prior mental health history of participants, social support network Research topic: Relationship between exercise and stress levels a. Dependent variable: Stress levels (measured through self-report scales or physiological markers like cortisol levels) b. Independent variable: Exercise regimen (intensity, duration, type) c. Extraneous variable: Dietary habits, work/school-related stressors
They didnt have stockpiles of extra food; they were dependent on the recent harvest.
Carbon Dating.
During the so-called Dark Ages, Spain was a bunch of independent kingdoms (countries) each with its own name. Some of them included:CastillaLeonEstremaduraNavarreValenciaAragon
Carbon density in the universe does not change over time as the total amount of carbon remains constant. The distribution of carbon throughout the universe may change due to elements being recycled through stellar processes, but the overall density of carbon remains relatively stable.